The paper presents the final results from the research project “Urban Abacus of Building Energy performances (Abaco Urbano Energeticodegli Edifci – AUREE)” aimed at supporting the renovation and energy efficiency enhancement of urban building heritage financed by the “Ricerca Sistema Elettrico (MISE) program”. The crux of AUREE project is a Web – GIS GeoBlog portal with customized semantic dashboards aimed at sharing information on urban built environment and promoting the participation of local stakeholders to its improvement. As the latest development of this research, a workflow integrating the AUREE portal with BIM authoring and an open source IoT platform has been implemented and applied to an experimental case study concerning a public building in Carbonia (Italy). The headquarters of the sustainable energy research center Sotacarbo was selected as case study. The presented results proved that it was possible to create an open system, which could be easily consulted by "non-expert" users as well, to make available real-time monitoring data obtained from IoT sensors measuring the energy consumption, the indoor environmental conditions and the usage profiles of public buildings. The proposed open system could also be suitable to be later used as an effective tool to support the rising “energy communities”.

One of the most important problems in the presence of epidemics and pandemics is an accurate prediction and preparedness. Scholars and experts argue that future pandemics and/or epidemics are almost inevitable events and is not whether next pandemics will happen, but when a new heath emergency will emerge. Epidemiologic models for prediction of Coronavirus Disease 2019 (COVID-19) have shown many limitations because of unpredictable dynamics of the new viral agent SARS-CoV-2 in environment and society. The main goals of this study are twofold: first, the analysis of anthropogenic activities and factors that may trigger pandemic threats; second, the planning of new directions for strategies to reduce risks that a pandemic threat emerges and/or in the initial phase to reduce vast diffusion and negative impact of new viral agents that can generate hazards and problems in public health, environment and socioeconomic systems. In particular, the investigation and understanding of sources and driving factors concerning the emergence and diffusion of new pandemics have critical aspects for strategic actions of forecast, prevention and preparation of effective policy responses to cope with next pandemic crises and health emergencies. Insights here endeavor, whenever possible, to clarify these problems to increase the knowledge of the sources and factor determining the emergence of new viral agents in order to design optimal response policies to face next pandemic diseases similar to COVID-19. .

The County of Avon in England was abolished in 1996 and replaced by four unitary planning authorities. Recently the authorities have been working closely to develop a West of England Joint Spatial Plan to facilitate better integration of policies on transport, housing, the environment etc. The Joint Spatial Plan team commissioned a multidisciplinary study to investigate whether the West of England has special characteristics of 'place' that engender shared interest and regional affinity, i.e. ‘sense of place’, to which emerging planning policies might positively respond. In this regard the present article is particularly focused on identifying whether the West of England has unique and unifying landscape characteristics, relating to topography, rural scenery, and flora and fauna, which combine with human experiences to distinguish the Region from adjoining areas. It is concluded that the West of England does indeed have real geographical integrity, being bound on all sides by attractive and prominent landscape features - the Mendip Hills, Cotswold Hills and Severn Estuary – that contribute to a sense of identity and belonging among its inhabitants. The alternating limestone ridges and broad clay vales that prevail across the Region’s heart, combined with low lying flatlands on the Region’s western fringe, provide further contrast with neighbouring regions, reinforcing the emotional bond to the landscape. Furthermore, it is suggested that the inhabitants of the West England show a particularly special environmental consciousness that sets them apart from neighbouring populations, complementing the influence of the physical environment in making the Region a special place to live.

UV-A (ca. 365 nm wavelength, a.k.a. 'black light') photocatalysis has been investigated to comprehensively mitigate odor and selected air pollutants in the livestock environment. This study was conducted to confirm the performance of UV-A photocatalysis on the swine farm. The objectives of this research were to (1) scale-up of the UV-A photocatalysis treatment, (2) evaluate the mitigation of odorous gases from swine slurry pit, and (3) test different UV sources, (4) evaluate the effect of suspended particulate matter (PM), and (5) conduct preliminary economic analyses. We tested UV-A photocatalysis at a mobile laboratory-scale capable of treating ~0.2 - 0.8 m3·s-1 of barn exhaust air. The targeted gaseous emissions of barn exhaust air were significantly mitigated (p < 0.05) up to 40% reduction of measured odor; 63%, 44%, 32%, 40%, 66%, and 49% reduction of dimethyl disulfide, isobutyric acid, butanoic acid, p-cresol, indole, and skatole, respectively; 40% reduction of H2S; 100% reduction of O3; and 13% reduction of N2O. The PM mitigation effect was not significant. Formaldehyde levels did not change, and a 21% generation of CO2 was observed. The percent reduction of targeted gases decreased as the airborne PM increased. Simultaneous chemical and sensory analysis confirmed that UV-A treatment changed the overall nuisance odor character of swine barn emissions into weaker manure odor with 'toothpaste and 'mint' notes. The smell of benzoic acid generated in UV-A treatment was likely one of the compounds responsible for the less-offensive overall odor character of the UV-treated emissions. Results are needed to inform the design of a farm-scale trial, where the interior barn walls can be treated with the photocatalyst, and foul air will be passively treated as it moves through the barn.

The present qualitative study delves into the sustainability practices within supply chain management, emphasizing insights garnered from leading global firms. With sustainability becoming an imperative across industries, understanding how top firms integrate sustainable practices within their supply chains offers valuable lessons for broader implementation. The investigation draws on interviews, case studies, and existing literature to identify key strategies, challenges, and outcomes associated with sustainable supply chain management. Findings reveal a comprehensive approach involving the adoption of green technologies, ethical sourcing, and stakeholder engagement, all of which contribute to enhanced environmental and social performance. The discussion highlights the critical role of leadership, innovation, and collaboration in driving sustainability, underscoring the need for continuous improvement and adaptation. Ultimately, the study concludes that while significant progress has been made, ongoing efforts and commitment are essential for achieving long-term sustainability goals in supply chain management.

The global energy crisis is a significant issue, particularly in developing countries like Ethiopia, where traditional fuels such as wood and charcoal are predominantly used to meet energy demands in urban and rural areas. Despite Ethiopia's substantial charcoal production, the use of inefficient charcoal cookstoves exacerbates fuel consumption, forest degradation, and environmental damage. To address this, various improved charcoal cookstoves (ICS) have been introduced in urban and semi-urban communities in Wereta Town, located in the Amhara Regional State of Ethiopia. This study aims to evaluate household perceptions and identify barriers to the adoption of ICS in the Wereta district. The assessment involved a survey of 308 households using a structured cross-sectional method and analyzed five types of improved cookstoves: clay stoves, insulated clay cylinder stoves, non-insulated rocket stoves, insulated rocket stoves, and non-insulated cylindrical stoves. The study employed various socio-economic variables and utilized both cross-tabulation frequency factors and linear regression statistical analysis through SPSS 20 to identify factors influencing the adoption of ICS. The linear regression results indicated that marital status, income level, family size, and age significantly impact ICS adoption (p-value < 0.05), while the place of cooking, educational level, household decision-making, and the role of societal stakeholders showed no significant impact. This suggests that ICS adoption is primarily influenced by household income levels and family size.

Cotton is the most ubiquitous and profitable fiber with diverse industrial and domestic applications. Grown in over 100 countries, it has a global market value of about $40 billion and employs over 350 million people from fields to textile mills, contributing about 7% of total labor-force recruitment in developing economies. Cotton has an indeterminate growth habit and an extensive tap root system affected by soil physicochemical and environmental conditions. There is a consensus among experts that conventional cotton cultivation still has a long way to attain sustainability, which is essential if cotton will maintain its competitive edge over other natural and synthetic fibers like hemp, polyesters, and rayon. Despite several efforts already committed to growing cotton sustainably, sustainability has eluded cotton cultivation globally because of the intense farm inputs (freshwater, pesticides, and heavy-duty equipment) needs, especially in developing economies. Some of the technological advancements towards achieving the goal of sustainable cotton cultivation include the development of new varieties, improved irrigation and mulching and precision agriculture techniques, application of remote sensing and Unmanned Aerial Systems (UAS) combined with image processing, and the introduction of autonomous and multi-purpose robotic platforms for growing and harvesting cotton. This review attempts to evaluate the successes already achieved by stakeholders in moving cotton towards sustainable production and identify areas where efforts are still needed to reach sustainable production and improved profitability goals for cotton with projections for future research directions.

Providing safely-managed sanitation/hygiene requires key competencies for education, training, service delivery, enterprise development and management, product and infrastructure design and development, construction and installation, governance, financing, research, etc. These strategic sanitation capacities will have to be built from higher education’s academic and professional programmes structured and designed to produce skilled and knowledgeable professionals and practitioners. This study aims to investigate the quality of the sanitation/hygiene management content of Environmental Health programmes; the adequacy of the existing central curricula; and the perception of environmental health sanitation/hygiene-trained professionals to determine the next phase for building knowledge and capacity of sanitation professionals in Nigeria through higher education institutions. A multilevel mixed method concurrent study was used for sampling and data collection with a multilevel perception analysis to examine the perceptions of students, lecturers and graduate alumni as well as employers/supervisors and clients/service-users of sanitation/hygiene related graduates. The main findings of this paper show limited understanding of the concepts of contemporary issues of sanitation/hygiene management; the central/national teaching and curricula were found to be seriously out of date; and clients/service-users were dissatisfied with the skills and knowledge levels of the graduates. The study concludes by recommending a national sanitation management higher education pathway to guide the provision of integrated sanitation/hygiene management education at higher education institutions (HEIs), to build effective, efficient, competent, and sufficient workforce for the country.

In contemporary design practices, there is a disconnect between the design techniques used for early-stage design experimentation and performance analysis, and those used for the manufacture and construction. This study addresses the problems in developing an integrated digital design workflow and provides a research framework for integrating environmental performance requirements with robotic manufacturing processes on a construction site. The proposed method enables the user to import a design surface, identify design parameters, set several environmental performance goals, and thereafter simulate and select a robotic building strategy. Based on these inputs, design alternatives are developed and evaluated, considering their robotically simulated constructibility, in terms of their performance criteria. To validate the proposed method, the design is evaluated in an experiment wherein a double-skin facade perforation is generated using the proposed methodology. The results suggest a heuristic feature to improve the simulated robotic constructibility. Moreover, the functionality of the prototype is demonstrated.

In Nitrate Vulnerable Zones (NVZ) site-specific techniques are needed to match N availability with durum wheat (Triticum turgidum subsp. durum Desf.) requirements. Enhanced-efficiency fertilizers (EEF) can improve efficient N supply and reduce leaching, thus contributing to sustainable agriculture. To study the effects of rates, sources and timings of nitrogen application, two-year field experiments were carried out at two Mediterranean NVZs of Central Italy (Pisa and Arezzo). The trial compared: i) two N rates: one based on the crop N requirements (NO), the other on the Action Programmes’ prescriptions of the two NVZ (NAP); ii) three N sources (urea, methylene urea (MU), and nitrification inhibitor (NI) 3,4-Dimethylpyrazole phosphate (DMPP); and two top-dressing timings (1st tiller visible and 1st node detectable). Grain yield and yield components were determined, together with N uptake. Results showed that: i) grain and biomass production were reduced with NAP at both locations; ii) urea performed better than slow-release fertilizers; iii) the best application time varied depending on N source and location: at Pisa enhanced-efficiency fertilizers achieved higher yields when applied earliest, while for urea the contrary was true; at Arezzo different N fertilizers showed similar performances between the two application timings. Different behaviors of top-dressing fertilizers at the two localities could be related to the diverse patterns of temperatures and rainfall. Therefore, optimal fertilization strategies vary according to environmental conditions.

In contemporary design practices, there is a disconnect between the design techniques used for early-stage design experimentation and performance analysis, and those used for the manufacture and construction. This study addresses the problems in developing an integrated digital design workflow and provides a research framework for integrating environmental performance requirements with robotic manufacturing processes on a construction site. The proposed method enables the user to import a design surface, identify design parameters, set several environmental performance goals, and thereafter simulate and select a robotic building strategy. Based on these inputs, design alternatives are developed and evaluated, considering their robotically simulated constructibility, in terms of their performance criteria. To validate the proposed method, the design is evaluated in an experiment wherein a double-skin facade perforation is generated using the proposed methodology. Initial results suggest a heuristic feature to improve the simulated robotic constructibility. Moreover, the functionality of the prototype is demonstrated.

Multiple social and environmental justice concerns are linked to urban form such as the distribution of socioeconomic class populations, healthcare spending, air pollution exposure, and human mobility. This study used 1m resolved LIDAR data to characterize land use in Salt Lake County, Utah and associate it with sociodemographic and air quality data at the census block group and zip code levels. We found that increasing tree cover was associated with higher per capita income and lower minority populations while increasing built cover was linked to lower per capita income and higher minority populations. Air quality showed less strong correlations, however, decreased non irrigated cover, increased built cover, and higher amounts of households living under poverty was related to higher long-term PM2.5 exposure. Several policy efforts have been undertaken to improve air quality and reduce negative health outcomes in Utah which are being informed by regulatory and research grade air quality sensors.

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system wide transition has not been researched sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric, diesel and gasoline powered vehicle models. We use the transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently we analyze the product systems’ vehicle production, use phase and End-of-Life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of the complete car based transportation in urban agglomerations and is able to analyze different transport sectors.

This study addresses the latent construct of attitudes towards environmental conservation based on study participant’s responses. We measured and evaluated the latent scale based on an 18-item scale instrument, over four experimental strata (N=945) in the US Virgin Islands and the Caribbean. We estimated the latent scale reliability and validity. We further fitted multiple alternative two-parameter logistic (2PL) and graded response models (GRM) from Item-Response Theory. We finally constructed and fitted equivalent structural and generalized structural equation models (SEM/GSEM) for the attitudinal latent scale. All scale measures (composite, alpha-based, IRT-based and SEM-based) were consistently and reliably valid measures of the study participants’ latent attitudes toward conservation. We found statistically significant differences among participant’s attributes relating to socio-demographic, physical and core environmental characteristics of participants. We assert that the nature of relationship between cognitive attitudes and individual as well as social behavior related to environmental conservation.

Wetlands are one of the crucial natural resources. They provide invaluable biodiversity resources, aid in water quality improvement, support ground water recharge, help in moderating climate change and support flood control. Environment is in the other hand, where we live and something, we are very familiar with our day to day life. Geographic Information Systems (GIS), Remote Sensing and Global Positioning System (GPS) were a useful tool for wetland and environmental change analysis and to improve on the classification accuracy. This study investigates population and environmental change of Jarmet wetland and its surrounding area change analysis over the period of 1972 to 2015. The purpose of this study was to show land use/ land cover change of Jarmet wetland and its surrounding environment over years as a response to population growth. For this purpose, multi-temporal satellite imageries (Landsat MSS 1972, TM1986, ETM+ 2000, 2005 and 2015 and SRTM 2000) were obtained and used for LULC change analysis, elevation analysis and change detection analysis. ERDAS Imagine 2015, ARC GIS 10.5.1, Global Mapper11, ENVI 5.0 and DNR Garmin softwares were used to process the image data and accuracy assessment analysis. The result of LULC showed that there is spatial reduction in wetland, forest, Shrubland and grassland in the period of 43 years (1972-2015) by -1,722.8 ha, -296.2 ha, -1,718.7 ha and -661.9 ha respectively, due to increase in the farmland and plantation area as a response to overpopulation, lack of environmental policy implementation and irresponsible for natural resource degradation. The accuracy assessment of LULC change are done for recent satellite image showed the overall accuracy of 84.06% with Kappa index 75.19% this means this classification is accurately classified and handle greater than 75% of error. Finally, this study suggests that create strictly natural resource conservation law, stopping illegal expansion of farmland, educating society about the value of natural resource especially wetland and create a source of income for society rather than farming.

Additive Manufacturing (AM) has been proving suitable to support or even replace traditional manufacturing in several industries, offering many advantages such as delivery time and reduction in terms of material waste, energy consumption and greenhouse gas (GHG) emissions. This study aimed to carry out a comparative assessment of the life cycle, from gate to gate, in the production of a low alloy carbon steel flange part using ER-90 wire. The methods utilized were Wire and Arc Additive Manufacturing (WAAM) and conventional manufacturing (CM) by forging, and comparative factors were energy demands, GHG emissions and generated solid waste. The total energy consumption in WAAM was 10,239.40 MJ, total carbon footprint in CO2 equivalent (CO2e) was 714.1 kgCO2e kg-1, and generated solid waste was 68.6 kg, respectively, 90%, 95% and 76% lower than consumption calculated in conventional manufacturing.

The automotive industry has witnessed a significant push towards increased fuel efficiency and reduced carbon emissions. Among many factors influencing overall vehicle efficiency, aerodynamic drag is a critical consideration, accounting for a substantial portion of fuel consumption. Our investigation focused on the drag coefficients of three prominent 2023 passenger car models – the Toyota Corolla, Hyundai Elantra, and Kia Soul. Using ANSYS Fluent to perform computational fluid dynamics, we demonstrated an inverse correlation between drag force and gas mileage, underlining the significance of aerodynamic design in fuel efficiency. Notably, the Hyundai Elantra emerged as the most aerodynamically efficient of the three vehicles, followed closely by the Toyota Corolla. In contrast, the boxier-designed Kia Soul exhibited significantly higher drag coefficients and drag forces, rendering it the least efficient of the trio. These findings emphasize the importance of environmental factors in car design. Shaping aerodynamic profiles directly impacts fuel efficiency, stability, and overall performance.

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photo-acoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3) and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a MIR (mid-infrared) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra is a common challenge for gas spectroscopy. To mitigate this, we use a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts-per-million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here in this proof-of-concept experiment we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3 and CH4 and with an absolute sensitivity of approximately 300(±50) ppm/V, 50(±5) ppm/V and 5(±2) ppm/V for water, ammonia and methane, respectively. Thus, demonstrating that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.

The metaverse, a virtual world comprising a collective virtual shared space where users interact with one another through avatars and computer-generated objects, aims to closely mimic our real world by integrating elements of Artificial Intelligence (AI), immersive reality, advanced connectivity, and Web3. As metaverse technologies gain momentum across multiple sectors, including animal farming, their potential for addressing complex challenges such as climate change and sustainability in precision food production systems becomes increasingly apparent. However, it is crucial to consider the ethical implications and the role of sensor data and livestock behavior analysis in developing metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations and harness the power of sensor data and behavior analysis could lead to a lack of credibility and insensitivity towards adopting metaverse technologies in the animal farming sector. It is essential to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics, socio-economic implications, and the potential for data-driven insights. Addressing diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. This groundbreaking paper ventures into unexplored territory, shedding light on the untapped potential of the metaverse for modern animal farming. While research on this topic is still in its infancy, we embark on a journey of visionary speculation, presenting a compelling technology forecast that envisions the extraordinary possibilities awaiting us in the future. By delving into the metaverse's transformative capabilities, we provide a glimpse into a world where animal farming transcends its traditional limitations and embraces a new era of efficiency, sustainability, and ethical practices.

Previous studies have shown, in the United States (U.S.), that non-White communities are exposed to significantly higher temperatures in urban environments than complementary White populations. Studies highlighting this disparity have usually been cross-sectional and are therefore “snapshots” in time. Using surface urban heat island (SUHI) intensity data, U.S. Census 2020 population counts, and a measure of residential segregation, this study performs a comparative analysis between census tracts identified as prevalent for White, Black, Hispanic and Asian populations and their thermal exposure from 2003 – 2018. The analysis concentrates on the top 200 most populous U.S. cities. SUHI intensity is shown to be increasing through time for all examined tracts. However, the increase is only statistically significant for White and Black prevalent zones. There is a 1.25K to ~2.00K higher degree of thermal exposure on average for non-White relative to White prevalent areas. When examined on an inter-city basis, White and Black prevalent tracts had the largest disparity, as measured by SUHI intensity, in New Orleans, LA, USA by &lt; 6.00K. Hispanic (&gt;7.00K) and Asian (&lt;6.75K) prevalent tracts were greatest in intensity in San Jose, CA, USA. To further explore temporal patterns, two models were developed using a Bayesian hierarchical spatial temporal framework. One models the effect of varying the percentages of each population group relative to SUHI intensity within all examined tracts. Increases in percentages of Black, Hispanic, and Asian populations contributed to statistically significant increases in SUHI intensity. White increases in population percentage lowered SUHI temperature intensity. Throughout all modeled tracts, there is a statistically significant 0.01K per year average increase in SUHI intensity. A second model tests the effect of residential segregation on thermal inequity across all examined cities. Residential segregation, indeed, has a statistically significant positive association with SUHI intensity from this portion of the analysis. Similarly, there is a statistically significant 0.01K increase in average SUHI intensity per year for all cities. Results from this study can be used to guide and prioritize intervention strategies and furthers urgency related to social, climatic, and environmental justice concerns.

The design and usage of the addressed combined fiber-optic sensors (ACFOS) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated. The main development trend of the combined fiber-optic sensors (CFOS), consisting of the fiber Bragg grating (FBG) and the Fabry-Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The addressed fiber Bragg structures (AFBS) usage instead of the FBG in the CFOS leads not only to significant cheapening of the sensor system due to microwave photonics interrogating methods, but also to increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of the greenhouse gases (СО2, NO2, CH4, OX) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of Karunen-Loeff transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, it allows determining the gas concentration at the AFBS address frequencies. The estimations have shown that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with the accuracy of 0.1–0.5%.

The presence of potentially pathogenic bacteria on veterinary clinic surfaces may be problematic. In this study we collected swab samples (Fisherbrand, double transport swabs with Stuart’s liquid medium) and water samples from five veterinary rehabilitation clinics. Swabs and water samples were transported to a microbiology lab for processing. At the lab swabs were used to inoculated Hardy’s Cdiff Banana broth (for Clostridium difficile [Cdiff]), and five different types of bacterial growth media, including: Hardy CHROM MRSA agar (methicillin resistant Staphylococcus aureus [MRSA], and S. pseudintermedius [SIM]), Mannitol Salt Agar (S. aureus), Eosin Methylene Blue agar (enterics [ENT]), Pseudomonas Isolation agar (Pseudomonas sp.), and Tryptic Soy Agar [TSA] (non-specific). The most prominent species cultured was Cdiff (on nearly 55% of swabs). Bacillus sp. and Enterics were encountered on nearly 35% of swabs, with MRSA and SIM on just over 10% of swabs. The most contaminated sample site was harnesses/life jackets used with the underwater treadmill (33% of swabs). Underwater treadmill water had total bacterial counts from 1.6 to 2.7 e3 cfu/ml. Of all bacterial species detected SIM tends to be more pathogenic for dogs. Targeted cleaning/disinfecting in these clinics could help reduce risks for both animals and caregivers utilizing these clinics.

How much is religion quantitatively involved in global climate politics? After assessing the role of the Conference of Parties to the United Nations Framework Convention on Climate Change from a normative perspective, this descriptive, transdisciplinary and unconventional study offers the first comprehensive quantitative examination of religious nongovernmental organizations that formally participate in its annual meetings, the largest attempts to solve the climate crisis through global governance. This study finds that although their numbers are growing, only about 3 percent of registered nongovernmental organizations accredited to participate in the conference are overtly religious in nature — and that more than 80 percent of those faith-based groups are Christian. Additionally, this study finds that religious nongovernmental organizations that participate in the conference are mostly from the Global North. The results call for greater participation of religious institutions in the international climate negotiations in order for society to address the planetary emergency of climate change.

In the construction business, which is known for its intricate and strategic nature, it is crucial to have efficient risk management in place. This is because various internal and external elements can cause disruptions to projects. Although the industry has had a historically negative reputation for risk analysis, the methodical process of identifying, analyzing, and managing risks has been more widely recognized and respected. This paper examines the historical progression of construction risk management, highlighting the need to identify and classify risks promptly. The study examines various risk factors related to finance, law, environment, and logistics. The study explores various risk management strategies, such as contract management, risk distribution, evaluation, mitigation, and sharing. The article discusses the significant impact of risk management software, such as Procore, PlanGrid, e-Builder, Autodesk BIM 360, and Oracle Primavera, on improving project results. The study finishes by highlighting the importance of proactive risk management in protecting against financial losses and maintaining a reputation in the construction business.

Climate changes challenges people’s life and sustainability. Environmental problems seem to derive from human behavior. Dentistry has a high environmental footprint that needs to be controlled worldwide. Thus, the change of behavior of dentists is a necessary condition to improve the environmental situation. In this study we use a system dynamics approach to analyze pro-environmental behavior in dentists. Systemic modelling in this case, allow us to make a clear prediction of how the population of non-eco dentists will change in the future if certain factors will be affected and how much time will be needed by the system of a group of dental professionals to become eco- dentists. The ECODENT model was designed with Vensim software. It was based on the influence of WOM (word of mouth) spread of information among colleagues and stakeholders (e.g. patients, auxiliary staff and community) and other factors such as dental income, state support, CaPex and OpEx, education hours, level of urbanization and time to react. By changing each one of them we can observe the changes in the groups of non-eco and eco dentists as well as the flow of the phenomenon. It is tested that by augmenting WOM parameter, state economic support or education hours, while diminishing CaPex and OpEx, changes in pro-environmental behaviors of dentist will be reported in a shorter time. The ECODENT model can be used to further study pro-environmentalism in different dental groups and associations and design relevant eco-friendly educational programs.

As in every workplace, human sustainability in dental enterprises is connected to air and water quality, eco-friendly and naturally designed working spaces and 4R’s culture. Purpose of this study is to access the preferences and knowledge on circular economy and green building construction in a sample of students and dentists in Greece. Students (N1=93) and dentists (N2= 126) filled in e-questionnaires from April to December 2022. The response rate was 17,88% for N1 and 1.94% for N2. Data revealed that both students and dentists lack knowledge on circular economy (N1=67.7%, N2=68,25%), EU regulations on amalgam disposal (N1=64.5%, N2=58.73%) and plastic recycling (N1=76.34%, N2=76,98) while they do recycle at home (N1= 80.6%, N2=82.54%) and have participated in voluntary environmental actions (N1=58.1%, N2=66.67%). Gender influences the importance of factors related to green dental practices, with women students being more prone to answer positively as important for increase costs for network changes (p=0.02), lack of environmental awareness of manufacturers (p=0.057), and poor wastewater management (p=0.01). Students from urban areas are more prone to answer positively for lack of state financial support (p=0.02), low level of green design in the building (p=0.03), low direct financial benefit of a green dental office (p=0.04), low benefit to the reputation of the green dental office (p=0.02), lack of continuing education training seminars on green dentistry (p=0.05). For dentists, no significant relationships were observed except of a weak positive relationship in increase in costs for changes related to utility networks (p=0.08) while increased wasteful energy (p=0.12) and waste of dental materials (p=0.19) seemed important only for dentists in urban areas. Women dentists were more prone to answer positively on wasting energy (p=0.024) and use of unapproved disinfection products (p=0.036). The findings contribute ideas and solutions on green dental office buildings and sustainable behaviors through educational activities and social perspective of factors such as age, experience in dentistry, gender, and urbanism. It also inspires future multi-disciplinary research on dental quality assurance, psychology of environmentalism, economics, and behavioral science in dentistry.

Observing air quality from sensors onboard light rail cars in Salt Lake County, Utah began as a pilot study in 2014 and has now evolved into a five-year state-funded program. This metropolitan region suffers from both elevated ozone levels during summer and high PM2.5 events during winter. Pollution episodes result predominantly from local anthropogenic emissions but are also impacted by regional transport of dust, chemical precursors to ozone, and wildfire smoke as well as being exacerbated by the topographical features surrounding the city. Two electric light-rail train cars from the Utah Transit Authority light-rail Transit Express (“TRAX”) system were outfitted with PM2.5 and ozone sensors to measure air quality at high spatial and temporal resolution in this region. Pollutant concentration data underwent quality control procedures to determine whether the train motion affected the readings and how the sensors compared against regulatory sensors. Quality assurance results from data obtained over the past year show that TRAX Observation Project sensors are reliable, which corroborates earlier preliminary validation work. Two case studies from summer 2019 are presented to illustrate the strength of the finely-resolved air quality observations: 1) an elevated ozone event and 2) elevated particulate pollution resulting from 4th of July fireworks. The mobile observations were able to capture spatial gradients as well as pollutant hotspots during both of these episodes. Sensors have been recently added to a third light rail train car, which travels on a north-south oriented rail line along which we were unable to monitor air quality previously. The TRAX Observation Project is currently being used to provide reliable pollutant data for health studies and inform urban planning efforts. Links to real-time data displays and updated information on the quality-controlled data from this study are available at https://atmos.utah.edu/air_quality/trax/.

The micropaleontological study (radiolarians and foraminifera) of the sediment core AMK-340, Reykjanes Ridge, North Atlantic, combined with the radiocarbon dating and Oxygen/Carbon isotopic record, provided data for the reconstruction of the summer paleotemperature on the water depth of 100 m, and paleoenvironments during the Termination I in the age interval of 14.5–8 ka. The response of the main microfossil species on the paleoceanographic changes within the Bølling-Allerød (BA) warming, the Younger Dryas (YD) cold event, and final transition to the warm Holocene was different. The BA warming was well reflected in the radiolarian and benthic but not planktic foraminiferal record. The high abundances of the cold-water radiolarian species Amphimelissa setosa as the Greenland/Iceland Sea indicator marked a cooling at the end of the BA and within the start of the YD at 13.2–12.3 ka. The micropaleontological and isotopic data together with the paleotemperature estimates for the Reykjanes Ridge at 60° N document that, after the warm BA, the middle YD ca. 12.5–12.2 ka was the next significant step toward the Holocene warming. Start of the Holocene interglacial conditions was reflected in abundant occurrence of the microfossils being indicators of the open boreal North Atlantic environments and lower oxygen isotope values indicating increasing warmth.

Wave energy converter (WEC) arrays deployed in coastal regions may create physical disturbances potentially resulting in environmental stresses. Presently, limited information is available on the nature of these physical disturbance or the resultant effects. A quantitative Spatial Environmental Assessment Tool (SEAT) for evaluating potential effects of wave energy converter (WEC) arrays on nearshore hydrodynamics and sediment transport is presented for the central Oregon coast (USA) through coupled numerical model simulations of an array of WECs. Derived climatological wave conditions were used as inputs to the model to allow for the calculation of risk metrics associated with various hydrodynamic and sediment transport variables such as maximum shear stress, bottom velocity, and change in bed elevation. The risk maps provided simple, quantitative, and spatially-resolved means of evaluating physical changes in the vicinity of a hypothetical WEC array in response to varying wave conditions. Near-field risk of sediment mobility was determined to be moderate in the lee of the densely spaced array, where the potential for increased sediment deposition could result in benthic habitat alteration. Modifications to the nearshore sediment deposition and erosion patterns were observed near headlands and topographic features, which could have implications for littoral sediment transport. The results illustrate the benefits of a risk evaluation tool for facilitating coastal resource management at early market marine renewable energy sites.

A simple approach to enable water-management agencies employing free data to achieve the goal of using a single set of predictive equations for water-quality retrievals with satisfactory accuracy is proposed. Multiple regression-derived equations based on surface reflectance, band ratios, and environmental factors as predictor variables for concentrations of Total Suspended Solids (TSS), Total Nitrogen (TN), and Total Phosphorus (TP) were derived using a hybrid forward-selection method that considers Variance Inflation Factor (VIF) in the forward-selection process. Landsat TM, ETM+, and OLI/TIRS images were jointly utilized with environmental factors, such as wind speed and water surface temperature, to derive the single set of equations. The coefficients of determination of the best-fitting resultant equations varied from 0.62 to 0.79. Among all chosen predictor variables, ratio of reflectance of visible red (Band 3 for Landsat TM and ETM+, or Band 4 for Landsat OLI/TIRS) to visible blue (Band 1 for Landsat TM and ETM+, or Band 2 for Landsat OLI/TIRS) has a strong influence on the predictive power for TSS retrieval. Environmental factors including wind speed, remote sensing-derived water surface temperature, solar altitude, and time difference (in days) between the image acquisition and water sampling were found important in water-quality parameter estimation.

Supermarkets in Port Harcourt (PH) city, Nigeria, predominantly rely on diesel electricity generation due to grid instability, leading to high cost of electricity prices. Although solar photovoltaic (PV) systems have been proposed as an alternative, these supermarkets have yet to adopt them, mainly due to high investment costs and a lack of awareness of the long-term financial and environmental benefits. This paper examines the technical and economic practicality of a PV system for these supermarkets using the PVsyst software and a spreadsheet model. Solar resources showed that PH has a daily average solar radiation and temperature of 4.21 kWh/m2/day and 25.73℃, respectively. Market Square, the supermarket with the highest peak power demand of 59.8 kW, and a 561 kWh/day load profile, was chosen as a case study. A proposed PV system with a power capacity of 232 kW, battery storage capacity of 34,021 Ah, a charge controller size of 100 A/560V, and an inverter with a power rating of 60V/75 kW has been designed to meet the load demand. The economic analysis showed a $266,936 life cycle cost, $0.12 per kWh levelized cost of electricity (LCOE), 4-year simple payback time, and a 20.5% internal rate of return (IRR). The PV system is feasible due to its positive net present value (NPV) of $165,322 and carbon savings of 582 tCO2/year.

Lower respiratory infections (LRI), upper respiratory infections (URI), and nutritional deficiency (ND) in under-5 children from low-middle-income countries has been at a high level for decades, despite efforts that have been realized in some countries. The study used data extracted from the global burden of diseases (GBD) to analyze the trends behavior between 1990 and 2019, and evaluate their association with maternal factors. Between 1990 and 2019, the incidence of LRI decreased between 1.2 percent and 1.3 percent (1.2-1.3%) in different female age groups, and by nearly 1 to 1.3% in those of the male gender. The incidence of URI did not decrease (0.0%) in any of their age groups. ND incidence declined around 0.7 to 4.9% in the under-5 children. LRI declined mostly in the female age groups (1.2 to 1.3%), while ND decreased more in the male gender (0.9 to 4.9%). Maternal risk factors were strongly associated to the incidence of the three conditions with over 90 percent. Maternal prevalence of HIV/AIDS and tuberculosis positively impacted the incidence of LRI, URI, and ND in Chadian under-5 children. Although the incidence of LRI and ND declined, URI incidence remained steady between 1990 and 2019 in Chadian under-5 children. To improve the health and provide a healthy growth of under-5 children, the maternal prevalence of HIV/AIDS and tuberculosis infection should be strongly addressed for a prompt accomplishment of the sustainable development goals.

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1-5 min) but low spatial resolution (2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with Deep Learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

UN Convention on Climate Change 2015 mandates reducing carbon footprint to reduce global warming. Considering environmental concerns, Electric Vehicles (EVs) spearhead the move towards green mobility. Niti Aayog (Indian Government) has envisaged an “EV only” scenario by 2030. Two wheelers dominate the Indian automobile industry with almost 80% market share. With the transportation sector accounting for 70% of world’s fossil fuels use, the need for a sustainable supply chain which addresses environment, economy and societal issues, has, of late, aroused interest in the business academic community and industries. Sustainability in business decision-making promotes efficiency, controls expenditure, enhances customer delight, increases sales and market share, optimises risk management strategy, and promotes profitability. This study intends to develop a conceptual framework after examining research gaps in sustainable supply chain management (SSCM) and allied topics like sustainability, green supply chain management, and vendor partnerships through collaborative efforts. Even the UN vide Goal 9 of sustainable goals pertaining to industry, highlights the importance of this framework.

Ovarian cancer (OC) represents a significant challenge in the realm of gynecological cancers, characterized by poor survival rates and complex etiology. In this review, we delve into the se-lected genetic, environmental, and hormonal factors underpinning OC development. We have reviewed scientific databases searching for ovarian cancer genetic and epigenetic factors. We have included studies based on their relevancy. As a result of exploring ovarian carcinogen-esis, this systematic review contains data collected from 126 various works. The role of prominent genetic players such as BRCA mutations, DNA repair mechanisms, un-derscores the intricate landscape of OC susceptibility. We explore Li-Fraumeni and Lynch syn-drome, which impart a heightened predisposition to OC development. Hormonal factors such as estrogen, progesterone, and androgens are also discussed. Environmental alterations, ranging from lifestyle influences on OC to microbiome dysbiosis and lifestyle factors such as obesity, al-cohol consumption, and physical activity intersect with genetic and epigenetic pathways, shaping the risk landscape for OC. Through a meticulous examination of current literature, this review provides a nuanced under-standing of the multifactorial nature of OC, emphasizing the need for holistic approaches to prevention, diagnosis, and treatment.

Given the context of global climate change, a worldwide increase in land surface temperature (LST) is anticipated, leading to the exacerbation and broadening of its impacts. This could jeopardize the environmental conditions in countries with a predominantly hot and harsh climate, such as Bahrain, one of the Cooperation Countries (GCC) nations. Conversely, Bahrain is currently experiencing significant population growth, leading to a surge in demand for land to accommodate the construction of additional residential developments. This circumstance allows investigation of the potential impact of land use and land cover alterations on the variation in Land Surface Temperature (LST). In order to accomplish this objective, a residential development project was executed within the timeframe spanning from 2013 to 2023. Four sets of Landsat 8 OLI/TIRS remote sensing datasets were selected, with each set corresponding to one of the four climate seasons. Each set consisted of two images: one capturing the study area before the commencement of the development process and the other depicting the study area after the completion of the development. The study area was analyzed by extracting the land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) on various dates. Subsequently, correlation and regression analysis were employed to examine the interrelationships among these three variables. The findings demonstrated a notable rise in the mean land surface temperature throughout the spring and autumn seasons following the conclusion of land development activities. The findings indicate a positive and robust association between LST and NDBI across all seasons. Moreover, this relationship strengthened following the completion of development activities in the area. Conversely, there was a negative correlation between LST and NDVI prior to the region's development, which transformed into a positive relationship post-development. These results provide empirical support for the notion that small-scale residential developments contribute to a notable increase in LST, primarily driven by the expansion of impervious surfaces in built-up areas. The findings can potentially contribute to the formulation of localized adaptation strategies for small-scale residential development projects.

Sado Island in Niigata prefecture, Japan is one of the first Globally Important Agricultural Heritage Systems (GIAHS) among developed countries and has since been involved in environmental conservation agriculture (ECA). While ECA is still in its early stage in Japan, it has proven to be effective in mitigating climate change in the agricultural sector; hence, this study analyzed the factors which could contribute to the ECA continuation among Sado Island farmers. The data revealed the prevalence of farmers’ cognitive dissonance between ECA and its mitigating effects on climate change. Exploratory factor analysis and ordinal regression confirmed the importance of perceived GIAHS involvement in the continuation of ECA. In addition, other identified factors affecting ECA continuation fall either on a macro-level (i.e., farmers’ awareness of their role in improving their environment) or micro-level (i.e., farmers’ differing farm optimizations). These perspectives highlighted the altruistic nature of the Sado Island ECA farmers by valuing the improvement of their local and global environment as their main reason to continue ECA, whereas their various farm management optimizations support this observed farmer altruism by providing avenues to increase yield with only a moderate paddy land area. This study thereby highlights the need to continuously develop sustainable strategies to maintain and improve a positive farmer mindset towards ECA.

Synthetic fiber ropes are frequently used as mooring lines in floaters supporting Floating Offshore Wind Turbines (FOWTs) due to their various advantages. Damages in these lines can lead to the disruption of the FOWTs normal operation and thus their early detection is crucial. Currently, this damage detection problem has been investigated exclusively for FOWT chain mooring lines. This study focuses on the damage detection in FOWT synthetic mooring lines under varying environmental-operational conditions (EOCs) via two types of vibration-based Structural Health Monitoring methods: those based on Multiple Models and those based on a single Functional Model. The methods are based on multivariate AutoRegressive or on scalar Transmittance Function (TF) AutoRegressive with eXogenous input models. All methods are evaluated through a Monte Carlo study involving 390 simulations with a 10MW wind turbine mounted on the semi-submersible OO-Star wind floater and two damage scenarios (10%, 14% stiffness reduction) to a single mooring line. The results using acceleration signals from only two measuring positions on the mooring line, indicate the great potential for robust SHM in FOWTs synthetic mooring lines with the TF model-based methods successfully detecting the considered early stage damage scenarios, even for EOCs not encountered in the methods training.

Recent advancements of computer and electronic systems have motivated the extensive use of intelligent systems for automation of agricultural industries. In this study, the temperature variation of the mushroom growing room is modeled through using a multi-layered perceptron (MLP) and radial basis function networks. Modeling has been done based on the independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP is found to be the second repetition with 12 neurons in the hidden layer and 20 neurons in the hidden layer for radial basis function networks. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural networks with radial basis function was selected as the optimal predictor for the behavior of the system.

The increasing frequency of male cancer coupled with the reduction in male fertility seen worldwide motivated us to seek a potential evolutionary link between these two phenomena, concerning the reproductive transcriptional modules observed in cancer and the expression of cancer-testis-antigens (CTA). The phylostratigraphy analysis of the human genome allowed us to link the early evolutionary origin of cancer by reproductive life cycles of the unicellulars and early multicellulars, potentially driving soma-germ transition, female meiosis and parthenogenesis of polyploid giant cancer cells (PGCCs), with the expansion of the CTA multi-families, very late during evolution. CTA adaptation was aided by retrovirus domestication in the unstable genomes of mammals, for protecting male fertility in stress conditions, particularly that of humans, as compensation for the energy consumption by a large complex brain which also exploited retrotransposition. We found that the early and late evolutionary branches of human cancer are united by the immunity-proto-placental network, which evolved in the Cambrian and shares stress regulators with the finely-tuned sex determination system. We further propose that social stress and endocrine disruption caused by environmental pollution with organic materials, which alter sex determination in male foetuses and further spermatogenesis in adults, bias the development of PGCC-parthenogenetic cancer by default.

In a saline semi-isolated Lagoon on the Cape Zeleny (White Sea), the annual dynamics of the vertical hydrological structure, and the seasonal dynamics of phytoplankton were traced, including species composition, and vertical distribution, abundance, nutrition type,and biomass.293 species and supraspecific taxa of algae and cyanobacteria were found. Most of the identified species are marine, and 38 species are freshwater. Taxonomic composition changed in the Lagoon during the year. Dinoflagellates dominated in winter and early June; unidentified cocci and flagellates in July; diatoms, dinoflagellates and unidentified cells in August; dinoflagellates in September; and unidentified cocci and flagellates in October–November. The abundance of algae also changed in the Lagoon during the year. The integrated biomass in the water column varied from 0.01 g C/m2 in January to 0.78 g C/m2 in early September. According to the abiotic parameters, the water column of the Lagoon is subdivided into several zones with different conditions and corresponding phytoplankton communities. Similarity between the communities of different horizons was 32-46% in summer and 7% in winter. The chemocline layer was the most populous. Almost the entire year it contained a maximum of phytoplankton biomass, 1-2 orders of magnitude higher than that in the overlying horizons. Comparison phytoplankton in the lagoon and in the nearby sea area was carried out. Despite the connection between the Lagoon and the sea phytoplankton structure in the surface water layer in the Lagoon and in the sea differs significantly in composition, quantitative parameters, and seasonal dynamics. The similarity of the communities in the sea and the Lagoon never exceeded 50%. In terms of biomass, the Lagoon lags behind the sea until mid-summer, but, starting from August, it outnumbers it, and the phytoplankton development in the Lagoon lasts longer, until late autumn. The phytoplankton structure and dynamics in the Lagoon and the sea by 24.5% was related to the daylight, water salinity, oxygen content and pH, but did not depend on water T, underwater illuminance, and depth. Oxygen content and pH are defined by PhP activity. Salinity serves as a vector of the vertical sequence of ecological niches. The daylight length seems to be the crucial factor of the seasonal PhP dynamics in the semi-isolated coastal stratified lakes and lagoons.

Marine litter, comprising persistent and human-made solid materials discarded in marine environments, poses a severe threat to Nigeria's coastal and marine ecosystems. Despite existing international, regional, and national regulations, ineffective enforcement and compliance have led to widespread environmental degradation (UNEP, 2021). This study examines the factors contributing to the proliferation of marine litter in Nigeria, focusing on regulatory gaps, insufficient waste management infrastructure, and the lack of public awareness. The findings reveal that the marine ecosystem is deteriorating due to inadequate waste disposal practices and limited understanding of the impacts of marine litter on both the environment and human health (UNEP, 2021). The paper concludes with recommendations for improving regulatory enforcement, enhancing public education, and investing in waste management infrastructure to address the growing crisis of marine litter. Throughout this article, the limited information on the impacts of marine pollution across Africa is highlighted, emphasizing the value of these insights despite their scarcity.

Higher traffic density in Russian cities and less uniform traffic due to congestions are reasons for increased emissions of pollutants contained in exhaust gases of vehicles. The paper presents the modeling results of pollutant dispersion in Magnitogorsk. The analysis of these results shows that now many blocks of flats and social infrastructure of the city are in a zone with an excess of maximum allowable concentration of pollutants. Having summarized the methodological framework formulated by Russian and foreign researchers, the authors systemized the existing methods used to decrease pollutants produced by vehicles. It is proposed to choose an optimal combination of such methods applying a developed mathematical model. By adopting an optimal combination of the methods, it is possible to ensure minimum air pollutant emissions from vehicles, allocating limited financial resources. The mathematical modeling of the solutions on changing traffic density and structure and implementing methods used to decrease pollutants on one of the Magnitogorsk crossroads with the heaviest traffic is used to draw a conclusion about efficiency of the designed model used as a tool for strategic planning of development and transformation of street networks.

A low-cost data logging platform is presented for environmental monitoring projects that provides long-term operation in remote or submerged environments. Three premade “breakout boards” from the open-source Arduino ecosystem are assembled into the core of the platform. The components are selected based on low-cost and ready availability, making the loggers easy to build and modify without specialized tools, or a significant background in electronics. Power optimization techniques are explained. The platform has proven to be highly reliable, and capable of operating for more than a year on standard AA batteries. The flexibility of the system is illustrated with two ongoing field studies recording drip rates in a cave, and water flow in a flooded cave system.

The study presented hereafter shows a new methodology to reveal traces of polyethylene (the most common microplastic particles, known as a structure of C2H4) in a sample of ocean water by the irradiation of a 50 keV, 1 µA electron beam. This is performed by analyzing the photon (produced by the electrons in water ) fluxes and spectra (i.e. fluxes as a function of photon energy) at different types of contaminated water with an adequate device and in particular looking at the peculiar interactions of electrons/photons with the potential abnormal atomic hydrogen (H), oxygen (O), carbon (C), phosphorus (P) compositions present in the water, as a function of living and not living organic organisms with a PO4 group RNA/DNA strands in a cluster configuration through a volumetric cells grid.

The rationale of this study is to explore underlying sustainability assessment constructs in the context of WCSM implementation as a key focus by manufacturing industries in the journey of achieving world-class status in their manufacturing operations. This study primarily intends to collect primary data from 143 plant managers and supervisors through the structured questionnaires at select manufacturing companies in south India on the basis of area-cum-purposive sampling technique. The survey questionnaire is designed exclusively to reflect the sustainability assessment dimensions in terms of social, economic and environmental issues to examine the impetus of WCSM implementation on the competitive advantage of manufacturing companies. The empirical findings of this study clearly revealed that sustainable manufacturing practices, sustainability programs, creation of sustainable products, safety for employee, community and product, operational efficiency and integration of sustainability goals in day-to-day manufacturing activities were considered to be the key indicators by the manufacturing firms in their never-ending journey towards becoming the world class. Moreover, the WCSM practices & techniques adopted by sample manufacturers have a substantial positive influence on the achievement of competitive advantage in their manufacturing and business operations.

The binding of actinide ions (Am(III) and U(VI)) in aqueous solutions by hybrid silica-hyperbranched poly(ethylene imine) nanoparticles (NP) and xerogels (XG) has been studied by means of batch experiments at different pH values (4, 7 and 9) under ambient atmospheric conditions. Both materials present relatively high removal efficiency at pH 4 and pH 7 (&gt; 70%) for Am(III) and U(VI). The lower removal efficiency for the nanoparticles is basically associated with the compact structure of the nanoparticles and the lower permeability and access to active amine groups compared to xerogels, and the negative charge of the radionuclide species formed under alkaline conditions (e.g. UO2(CO3)34- and Am(CO3)2-). Generally, the adsorption process is relatively slow due to the very low radionuclide concentrations used in the study and is basically governed by the actinide diffusion from the aqueous phase to the solid surface. On the other hand, the adsorption is favored with increasing temperature assuming that the reaction is endothermic and entropy-driven, which is associated with increasing randomness at the solid-liquid interphase upon actinide adsorption. To the best of our knowledge, this is the first study on hybrid silica-hyperbranched poly(ethylene imine) nanoparticle and xerogel materials used as adsorbents for americium and uranium at ultra-trace levels. Compared to other adsorbent materials used for binding americium and uranium ions, both materials show far higher binding efficiency. Xerogels could remove both actinides even from seawater by almost 90%, whereas nanoparticles could remove uranium by 80% and americium by 70%. The above, along with their simple derivatization to increase the selectivity towards a specific radionuclide and their easy processing to be included in separation technologies, could make these materials attractive candidates for the treatment of radionuclide/actinide contaminated water.

This study investigates how a range of economic, environmental, and social indicators have influenced the concept of inclusive growth in 19 member countries of the Asian Cooperation Dialogue over the period spanning from 1995 to 2021. To analyze these relationships, the research employs the Driscoll Kraay's Standard Errors regression technique, designed to account for factors such as cross-sectional dependence, hetero-scedasticity, and autocorrelation within the data. Furthermore, various preliminary tests were conducted to assess the data for cross-sectional dependence, slope heterogeneity, heteroscedasticity, and autocorrelation. Additionally, the Westerlund cointegration test was employed to evaluate the potential long-term equilibrium relationships among the variables. The findings reveal that the influence of independent variables on the dependent variable, which in this case is the level of inclusive growth (ING), differs significantly among three distinct income categories: lower-middle-income countries (LMYCs), upper-middle-income countries (UMYCs), and high-income countries (HYCs). Notably, the results underscore the overall statistical significance and robust fit of the regression model for all three income groups. One of the primary contribu-tions of this research is the provision of empirical evidence concerning the role played by fishery and aquaculture production in fostering inclu-sive growth in the Asian context. This research also highlights the trade-offs between economic development and environmental sustainability in terms of trade openness, agriculture, forestry and fishing, ecological footprint, and renewable energy utilization. Enhancing inclusive growth in Asia requires improving fishery and aquaculture management, diversifying economic activities, reducing ecological footprint, and increasing renewable energy utilization. The paper suggests some future work directions for extending the analysis to other regions and indicators, as well as incorporating dynamic panel data models and causality tests. The paper also suggests some policy implications for fostering inclusive growth in Asia through regional cooperation, capacity building, technology transfer and green financing.

Aquaculture is expected to play a vital role in solving the challenge of sustainably providing the growing world population with healthy and nutritious food. Pathogen outbreaks are a major risk for the sector, so early detection and a timely response are crucial. This can be enabled by monitoring of pathogen levels in aquaculture facilities. This paper describes a photonic biosensing platform based on silicon nitride waveguide technology with integrated active components, which could be used for such applications. Compared to the state of the art, the current system presents improvements in terms of miniaturization of the Photonic Integrated Circuit (PIC), and the development of wafer level processes for hybrid integration of active components and for material-selective chemical and biological surface modification. Furthermore, scalable processes for integrating the PIC in a microfluidic cartridge have been developed, as well as a prototype desktop readout instrument. Three bacterial aquaculture pathogens (Aeromonas salmonicida, Vagococcus salmoninarum, and Yersinia ruckeri) were selected for assay development. DNA biomarkers were identified, corresponding primer-probe sets designed, and qPCR assays developed. The biomarker for Aeromonas was also detected using the hybrid PIC platform. This is the first successful demonstration of biosensing on the hybrid PIC platform.

Air pollution is a critical issue impacting urban environments, leading to severe health problems and environmental degradation. This comprehensive review examines the potential of green systems—specifically green walls, active green walls, and urban greenery systems—to mitigate atmospheric pollutants such as particulate matter (PM), volatile organic compounds (VOCs), and carbon dioxide (CO2). By systematically analyzing 44 recent studies, the review highlights the pollutant capture efficiency of various green technologies and plant species in both indoor and outdoor settings. Active green walls, particularly those utilizing plant species such as Chlorophytum comosum and Sansevieria trifasciata, were found to be highly effective, with VOC reduction efficiencies up to 96.34%, PM reductions by 65.42%, and CO2 reduction rates reaching 4.8% under optimal conditions. The review identifies key strengths in current research, including diverse experimental setups and the use of sophisticated measurement techniques, but also notes significant limitations such as variability in experimental conditions and a lack of long-term performance data. The study underscores the importance of proper maintenance to sustain green systems' efficacy and highlights the potential issue of pollutant resuspension, which remains under-researched. Practical implications for urban planning are discussed, advocating for the integration of effective green systems into urban infrastructure to enhance air quality and public health. Recommendations for future research include the need for standardized metrics, long-term studies, economic feasibility analyses, and real-world validation of simulation models to better understand and optimize green systems for urban air pollution mitigation.

This paper provides an in-depth exploration of the role of Big Data and Artificial Intelligence (AI) in advancing dairy farming towards net zero emissions, a critical goal in the face of the global climate crisis. The study emphasizes how these technologies significantly enhance the management of greenhouse gas (GHG) emissions and optimize resource use, thereby contributing to environmental sustainability in agriculture. A key aspect of this transition is the alignment with international climate commitments, such as the Paris Agreement, which are instrumental in steering global efforts toward emission reduction and mitigating climate change. The integration of Big Data and AI in dairy farming emerges as a powerful tool to reduce the sector's environmental impact while sustaining economic growth. The paper delves into the specific applications of these technologies in emission management, including predictive analytics for feed optimization, manure management, and energy efficiency enhancements. It also addresses the broader implications of technological integration in dairy farming, considering aspects like benchmarking standards, data privacy, and the role of policy in fostering sustainable practices. The study underscores the challenges inherent in adopting these advanced technologies, including the need for improved farmer training, data quality, and compatibility with existing systems. It also advocates for enhanced policy frameworks that support sustainable practices, encourage technological adoption, and balance economic viability with environmental responsibility. This comprehensive analysis offers valuable insights into harnessing digital technologies for climate change mitigation and delineates a path for the dairy industry towards achieving net zero emissions, thereby contributing significantly to global environmental sustainability efforts.

Parental stresses are normal responses to raising children. They are affected by stresses parents and children accumulate and bring to their interrelations. Background factors like economic difficulties or the relations between the parents may affect parental stresses as well as demographic and environmental factors like noise and access to urban parks. Most studies on parental stress are based on a verified psychological questionnaire. We suggest using frequency domain heart rate variability index (HRV) to measure parental stress enabling, by thus, the measurement of physiological aspects of stress and risk to health. Parental stress is measured as the difference between HRV accumulated at home while staying with the children and without the husband and HRV measured in the neighborhood while staying without the children and the husband. We use the index to compare differences among Muslim and Jewish mothers in exposure to maternal stress at their homes and to expose the factors that predict differences in maternal stress. We found that Muslim mothers suffer from home-related maternal stress while Jewish mother do not. Number of children and ethnically related environmental aspects predict differences in maternal stress between Muslim and Jewish mothers. Muslims' lower access to parks stems from lack of home garden and parks in their neighborhoods in the Arab towns but mainly by restrictions on Muslim women's' freedom of movement to parks. Despite differences in levels of noise at home and in the status of the mother in the household, these factors did not predict differences in parental stress. Instead, the study highlights the crucial role of greenery and freedom of movement to parks in moderating home-related maternal stress.

This article explores the cutting-edge advancement of gypsum and cement building boards infused with shape-stabilized n-octadecane, an organic phase change material (PCM). The primary focus is on improving energy efficiency and providing electromagnetic interference (EMI) shielding capabilities for contemporary buildings. The research investigates the integration of these materials into construction materials, using red-mud Carbon Foam as a stabilizer for n-octadecane (OD@CCF). Various analyses, including microstructural examination, porosity assessment, and additive dispersion, were conducted using X-ray microtomography and density measurements. Thermal conductivity measurements demonstrated the enhancement of composite boards as the OD@CCF content increased, while mechanical tests indicated an optimal additive content of up to 20%. The thermal-regulated capabilities of these advanced panels were evaluated in a custom-designed room model equipped with a homemade environmental chamber, illustrating a consistent temperature environment during heating and cooling cycles. The incorporation of OD@CCF into cement boards exhibited improved thermal energy storage properties. Moreover, the examined composite boards displayed efficient electromagnetic shielding performance within the frequency range of 3.2–7.0 GHz, achieving EMI values of approximately 18 and 19.5 dB for gypsum and cement boards, respectively, meeting the entry-level value required for commercial applications.

Detecting volatile organic compounds (VOCs) emitted from different plant species and their or-gans can provide valuable information about plant health and environmental factors that affect them. For example, limonene emission can be a biomarker to monitor plant health and detect stress. Traditional methods for VOC detection encounter challenges, prompting the proposal of novel approaches. In this study, we proposed integrating electrospinning, molecular imprinting, and conductive nanofibers to fabricate limonene sensors. In detail, polyvinylpyrrolidone (PVP) and polyacrylic acid (PAA) served here as fiber and cavity formers, respectively, with multi-walled carbon nanotubes (MWCNT) enhancing conductivity. We developed one-step monolithic molecularly imprinted fibers, where S(-)-limonene was the target molecule using electrospinning technique. The functional cavities were fixed using UV curing method, followed by a target mol-ecule washing. This procedure enabled the creation of recognition sites for limonene within the nanofiber matrix, enhancing sensor performance and streamlining manufacturing. Humidity was crucial for sensor working, with optimal conditions at about 50% RH. The sensors rapidly re-sponded to S(-)-limonene, reaching a plateau within 200 seconds. Enhancing fiber density im-proved sensor performance, resulting in a lower limit of detection (LOD) of 137 ppb. However, excessive fiber density decreased accessibility to active sites, thus reducing sensitivity. Remarka-bly, the thinnest mat on the fibrous sensors created provided the highest selectivity to limonene (Selectivity Index: 72%) compared to other VOCs, such as EtOH (used as a solvent in nanofiber development), aromatic compounds (toluene), and two other monoterpenes (α-pinene and linalo-ol) with similar structure. These findings underscored the potential of the proposed integrated approach for selective VOC detection in applications such as precision agriculture and environ-mental monitoring.

Eco-innovation denotes developing green practices using environmentally friendly innovative approaches or technologies. Although, eco-innovation has been broadly applied in different in-dustries, such as low-carbon production and manufacturing, how to implement such innovation in education for sustainable development (ESD) has rarely been studied. Therefore, this research considered a reflective and cooperative learning approach to science, technology, engineering, arts, and math (STEAM) education. A case study examined Wanluan Township of Pingtung County in southern Taiwan. Four departments’ students and teachers at Meiho University were involved in situated learning. Students of hospitality management played farm owners who en-gaged in organic agriculture to produce food and beverages. These agricultural products were farm-to-table, cooked and served for customers in a natural dining setting through the students’ teamwork. Students of tourism, sports & leisure management, and food science & nutrition played tourists in a self-guided travel context, who engaged in acts such as visiting buildings to understand heritage while observing that the houses were still in use a dwelling. This encouraged reflection on the importance of cultural preservation. According to the results from the role-play farm owners and tourists, eco-innovation can represent a sustainable marketing strategy for im-proving the local-community economy and can be implemented in a practical situation in STEAM. The goal of ESD for 2030 — societal transformation — is to foster students’ responsible behavior and attitudes in a personally authentic manner, thereby fostering sustainability learning and understanding.

Measures in the SARS-CoV-2 pandemic were based on rough ideas regarding transmission routes of pathogens. Quantified models of physical transmission routes are mostly lacking, a gap to be filled. Vaccines and medicines, important, are not studied here. We first survey main routes, from primary production in the alveoli and intestines to emissions, environmental routes, to exposure and alveolar infection. Next, specific routes are modelled, mostly at a preliminary state, open to systematic improvement. Starting from a standardized emitter, modelling results show extreme differences in potential exposure, in a range covering up to 4 orders of magnitude. The outcomes are pathogen-specific, already different between SARS-CoV-2 and influenza. Extreme exposures may result in smaller spaces; with lower ventilation rates; with a high density of emitting persons per m3; who stay there for several hours; and visitors staying more than a few minutes. In spaces where a build-up of concentrations is low, exposures are low, lowest in open air situations. A main conclusion for the next pandemic is that a quantified model can give strong guidance on where measures are primarily due. For SARS-CoV-2, ventilation can be improved short-term. Longer-term, effective ventilation rules and adaptation of buildings may reduce high exposures substantially.

The paper presents the main experimental capabilities of the automatic system for the study of the interaction between porous media and atmosphere. This system is one of the possible variants of patent 2-241-477. The paper shows the result of a drying and wetting study of a tailings sample. The sample is 32 cm in height and 29 cm in diameter. The saturation and drying process was carried out at 26 degrees Celsius at the sample surface, an air velocity of 2 m/s and a relative humidity of 60%. The recharge applied to the column surface was 0.3 cm3/cm2/minute. The results show the effectiveness of the system in controlling the spatial-temporal variation of different physical properties of the porous medium in the vertical and surface profile. The controlled variables include volumetric water content, suction, relative humidity, sample weight variation due to recharge or drying, shrinkage, temperature, etc. The experiment shows how the tailings saturate very quickly but dry very slowly. Tailings consolidation occurs in the drying and wetting processes, being higher in the first one.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

This article challenges the notion of the randomness of mutations in eukaryotic cells by unveiling stress-induced human non-random genome editing mechanisms. To account for the existence of such mechanisms, I have developed molecular concepts of the cell environment and cell environmental stressors and, making use of a large quantity of published data, hypothesized the origin of some crucial biological leaps along the evolutionary path of life on Earth under the pressure of natural selection, in particular, 1) virus-cell mating as a primordial form of sexual recombination and symbiosis; 2) Lamarckian CRISPR-Cas systems; 3) eukaryotic gene development; 4) antiviral activity of retrotransposon-guided mutagenic enzymes and finally; 5) the exaptation of antiviral mutagenic mechanisms to stress-induced genome editing mechanisms directed at “hypertranscribed” endogenous genes. Genes transcribed at their maximum rate (hypertranscribed), yet still unable to meet new chronic environmental demands generated by “pollution”, are inadequate and generate more and more intronic retrotransposon transcripts. In this scenario, RNA-guided mutagenic enzymes (e.g. AID/APOBECs), which have been shown to bind to retrotransposon RNA-repetitive sequences, would be surgically targeted by intronic retrotransposons on opened chromatin regions of the same “hypertranscribed” genes. RNA-guided mutagenic enzymes may therefore “Lamarkianly” generate single nucleotide polymorphisms (SNP) and copy number variations (CNV), as well as transposon transposition and chromosomal translocations in the restricted areas of hyperfunctional and inadequate genes, leaving intact the rest of the genome. CNV and SNP of hypertranscribed genes may allow cells to surgically explore a new fitness scenario, which increases their adaptability to stressful environmental conditions. Like the mechanisms of immunoglobulin somatic hypermutation, non-random genome editing mechanisms may generate several cell mutants, and those codifying for the most environmentally-adequate proteins would have a survival advantage and would therefore be Darwinianly selected. Non-random genome editing mechanisms represent a link between environmental changes and biological novelty and plasticity, and provide a molecular basis to reconcile gene-centered and “ecological” views of evolution.

The building sector has a big potential to reduce the material resource demand needed for building construction and therefore, greenhouse gas (GHG) emissions. Digitalisation can help to make use of this potential and improve sustainability throughout the entire building’s life cycle. One way to address this potential is through the integration of Life-Cycle Assessment (LCA) into the building process by employing Building Information Modelling (BIM). BIM can reduce the effort needed to carry out an LCA and therefore facilitate the integration into the building process. A review of current industry practice and scientific literature shows two main approaches to address BIM-LCA integration. Either the LCA is performed in a simplified way at the beginning of the building process, or it is done at the very end when all the needed information is available, but it is too late for decision-making. One reason for this is the lack of methods, workflows and tools to implement BIM-LCA integration over the entire building process. Therefore, the main objective of this study is to develop an integrated BIM-LCA workflow implemented into a method for the whole building process using an existing structure for cost estimation. A tool is created and used in a case study in Switzerland to test the developed approach. The results of this study show that LCA can be performed continuously in each building phase over the entire building process using existing BIM modelling techniques. The main benefit of this approach is that the re-work caused by the need for re-entering data and the usage of many different software tools that characterise most of the current LCA practices is minimised. Furthermore, decision-making, both at the element and building levels, is supported.

Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

Background: This study examines the relationship between energy usage, health issues, and pro-environmental behavior (PEB) in Kyrgyzstan, amidst the country's commitment to transition from coal-based energy to renewable sources in line with the Paris Agreement. The purpose is to investigate citizens' attitudes towards PEB and their intentions to engage in environmentally friendly actions, focusing on gas, electricity, and coal. Methods: Drawing upon the Theory of Planned Behavior (TPB) framework, a survey was conducted among 1,455 respondents to explore attitudes towards PEB and energy sources' impact on health issues. Results: Decarbonization efforts in Kyrgyzstan and Central Asia are in their early stages, with coal remaining a primary energy source. The study emphasizes the importance of governmental policies and citizen action in achieving decarbonization goals. Rising electricity costs outweigh the increase in indirect energy costs for food, posing challenges for households in adapting to changing energy dynamics. Conclusions: Targeted interventions and communication strategies are crucial to promote pro-environmental behavior and facilitate the transition to sustainable energy sources. Understanding the relationships between health concerns, air pollution awareness, PEB, and energy source choices can inform policymakers and organizations in their efforts to ensure a sustainable and healthy future for Kyrgyzstan and other Central Asian countries.