Hasil untuk "Environmental technology. Sanitary engineering"

Tanja E. J. Vos, Tijs van der Storm, Alexander Serebrenik et al.

Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on advances in software engineering, yet the field is too often treated as a supportive digital component rather than as a strategic, enabling discipline. In policy frameworks, including major European programmes, software appears primarily as a building block within other technologies, while the scientific discipline of software engineering remains largely absent. This position paper argues that the long-term sustainability, dependability, and sovereignty of digital technologies depend on investment in software engineering research. It is a call to reclaim the identity of software engineering.

Dario Cottafava, José R. Nicolás-Carlock, Marcel Llavero-Pasquina

This study investigates the interconnectivity of firms and Environmental Justice Organizations (EJOs) involved in socio-environmental conflicts worldwide, using data from the Environmental Justice Atlas (EJAtlas). By constructing a multilayer network that links firms, conflicts, and EJOs, the research applies social network analysis to evaluate the simultaneous involvement of these actors across multiple disputes. Both projected networks of firms and EJOs have been analysed by aggregating nodes by categories and countries to reveal structural differences. Findings reveal a stark contrast between the interconnectedness of firms and EJOs. Multinational corporations form a cohesive global network, enabling them to coordinate strategies and exert influence across regions. Conversely, EJOs are fragmented, often operating in isolated clusters with limited interconnection but forming a robust, decentralized and self-organized global network. Firms network present a strong dependence on pertaining conflict category while EJOs network does not depend on conflict category. This structural difference suggests a risk of systemic and structural coordination for firms towards exploitative expansion while EJOs dynamics seems to be led by a white blood cells defense-like mechanism. While fragmentation may represents a critical challenge for social movements, decentralization and self-organization show a more diffuse global networks supported by a limited number of central hub able to build stronger global alliances to effectively counter the power dynamics of transnational corporations. By providing robust evidence of these networks, this research contributes to discuss how structural differences in global coordination for companies and EJOs directly derives as emergent properties depending on the purpose of the network itself, sectorial expansion for firms while ecosystem preservation for EJOs.

Patricia G. F. Matsubara, Tayana Conte

Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and practice community may face when adopting EtD frameworks, highlighting the need for more comprehensive criteria in our recommendations to industry practitioners.

Hao He, Timothy P Canty, Russell R Dickerson et al.

Between June 6 and 8, 2023, wildfires in Quebec, Canada generated massive smoke plumes that traveled long distances and deteriorated air quality across the Northeastern United States (US). Surface daily PM _2.5 observations exceeded 100 µ g m ^−3 , affecting major cities such as New York City and Philadelphia, while many areas lacked PM _2.5 monitors, making it difficult to assess local air quality conditions. To address this gap, we developed a WRF-CMAQ-BenMAP modeling system to provide rapid, spatially continuous estimates of wildfire-attributable PM _2.5 concentrations and associated health impacts, particularly benefiting regions lacking air quality monitoring. CMAQ simulations driven by two wildfire emissions datasets and two meteorological drivers showed good agreement with PM _2.5 observations, with linear regression results of R ^2 ∼0.6 and slope ∼0.9. We further quantified uncertainties introduced by varying emissions and meteorological drivers and found the choice of wildfire emissions dataset alone can alter PM _2.5 simulations by up to 40 µ g m ^−3 (∼40%). Short-term health impacts were evaluated using the BenMAP model. Validation against asthma-associated emergency department (ED) visits in New York State confirmed the framework’s ability to replicate real-world outcomes, with ED visits increased up to ∼40%. The modeling results identified counties most severely affected by wildfire plumes, the majority of which lack regulatory air quality monitors. Our approach highlights the value of integrated modeling for identifying vulnerable populations and delivering timely health burden estimates, regardless of local monitoring availability.

Courtney Grimes, Ramón A Alvarez

C. J. Horwell, S. Ravenhall, R. Clarkson et al.

Abstract Volcanic eruptions produce plumes of ash, gas and aerosols that present a risk to aviation at all standard flight levels. Here, we investigate atmospheric dispersal of volcanic emissions, whether and how they infiltrate aircraft, and whether ground-level public health exposure thresholds can be related to the pressurised cabin environment. We then review the limited evidence for physical and mental health, and behavioural impacts, resulting from volcanic emissions entering aircraft. Serious health risks are considered low for healthy individuals, but respiratory irritation is likely for a high exposure scenario to sulfur dioxide (SO2). Asthmatics are particularly sensitive to SO2, with even relatively low, short exposures, potentially resulting in severe respiratory impacts. Negative group behaviours are not expected but individual distress is possible. Communicating this evidence to the aviation industry may result in more informed decision-making on flightpath alterations and triggering of emergency protocols, both before and during volcanic emission encounters.

Steve Dann Camargo Hinostroza, Carmen Andrea Taza Rojas, Diana Lizet Poma Limache and Camila Jimena Poma Romero

The objective of the research was to determine the water quality index of Lake Chinchaycocha, which has faced pollution problems for several years. To do this, we worked with data from ten water quality monitoring points collected by the National Water Authority (ANA) during the period 2019-2023, after which the water quality index (ICA-PE) was calculated by analyzing a total of 12 parameters, using the Water Quality Standard (ECA) for water category 4 E1 (lagoons and lakes). The results of the physicochemical parameters indicated that the values of total nitrogen exceed the limits established in the ECA in 82% of the data obtained, pH in 13%, and phosphorus in 1%. In the evaluation of inorganic parameters, data from the LChin1S monitoring point showed that lead and zinc levels exceeded the values established in the ECA by 8% and 3%, respectively. Regarding the ICA-PE of the dry and wet seasons, it was determined that both present a good quality according to their averages and with the results obtained from the ICA-PE in a general way, it is concluded that Lake Chinchaycocha has a good water quality having total nitrogen as the main pollutant.

W. Furian, T. Sauter

<p>This study investigates simulated glacial lake outburst floods (GLOFs) at five glacial lakes in the Everest region of Nepal using the three-dimensional model OpenFOAM. It presents the evolution of GLOF characteristics in the 21st century considering different moraine breach scenarios and two Shared Socioeconomic Pathways scenarios. The results demonstrate that in low-magnitude scenarios, the five lakes generate GLOFs that inundate between 0.35 and 2.23 km<span class="inline-formula">²</span> of agricultural land with an average water depth of 0.9 to 3.58 m. These GLOFs reach distances of 59 to 84 km, affect 30 to 88 km of roads or trails, and inundate 183 to 1699 buildings with 1.2 to 4.9 m of water. In higher scenarios, GLOFs can extend over 100 km and also affect larger settlements in the foothills. Between 80 and 100 km of roads, 735 to 1989 houses and 0.85 to 3.52 km<span class="inline-formula">²</span> of agricultural land could be inundated, with average water depths of up to 10 m. The high precision of the 3D flood modeling, with detailed simulations of turbulence and viscosity, provides valuable insights into 21st-century GLOF evolution, supporting more accurate risk assessments and effective adaptation strategies.</p>

Hayato Ishida, Amal Elsokary, Maria Aslam et al.

Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quantum communication networks, specifically quantum key distribution network proposals, to model and demonstrate the evolution of quantum key distribution network architectures. Leveraging Orthogonal Variability Modelling and Systems Modelling Language as candidate modelling languages, the study creates traceable artefacts to promote modular architectures that are reusable for future studies. We propose a variability-driven framework for managing fast-evolving network architectures with respect to increasing stakeholder expectations. The result contributes to the systematic development of viable quantum key distribution networks and supports the investigation of similar integration challenges relevant to the broader context of quantum systems engineering.

Tomi Suomi, Petri Ihantola, Tommi Mikkonen et al.

Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.

Yi Lu

Currently, the main method of disposal of municipal solid waste is sanitary landfill. However, a significant amount of leachate is produced during the landfilling process, which can lead to a series of environmental and economic problems. This paper reviews the research on Fenton and Fenton-like oxidation technologies in the treatment of waste leachate. The paper first introduces the characteristics of waste leachate and its hazards to humans and the ecological environment. Then, it will be followed by the technical principles and characteristics of the classic Fenton oxidation process. Starting from the limitations of classic Fenton in practical applications, the paper introduces combined oxidation technologies of ultrasound, microwave, light, and electricity with Fenton, and describes the different characteristics and optimal reaction conditions of various oxidation technologies. In practical applications, the most suitable oxidation technology can be selected based on the water quality characteristics of the leachate. Finally, introduce the development prospects and improvement methods of ultrasound-Fenton, microwave-Fenton, photo-Fenton, and electro-Fenton.

Helen Kupriyashkina, L. Pylypenko, Olena Sevastyanova et al.

The composition of microbial contaminants of soil samples polluted with oil and oil products from oil depots of ports in southern Ukraine was investigated, and the possibility of their bioremediation by microorganisms present in the soil was determined. The microbiological landscape of the soil contaminated with oil and oil products was established, the quantitative and qualitative characteristics, group and dendrological composition of microorganisms as well as their potential ability to biodegrade petroleum hydrocarbons were determined. The degree of sanitary and ecological contamination of the samples was characterized by the number of the main groups of microorganisms – mesophilic aerobic and facultative anaerobic microorganisms (MAFAnM), molds, yeasts, as well as the dominance of MAFAnM by 3-5 orders among the studied groups of microorganisms. According to MAFANM, the number of thermophilic bacteria, titers of nitrifying bacteria , E. coli, Clostridium perfringens , bacteria of the genus Proteus , and the degree of oil contamination, the soil samples studied are characterized as contaminated and heavily contaminated. According to the study of morphological, tintorial, cultural, biochemical properties, 130 species were identified and 9 morphogroups of bacteria in oil-contaminated soil samples were determined. A dendrogram was constructed based on the set of studied properties of the isolated microorganisms. According to the results of the screening, the microorganisms isolated from the contaminated soil samples are capable of biodegradation of long-chain alkanes of petroleum hydrocarbons. The identified groups of microorganisms can be arranged in the following order of increasing indicator: Bacillus subtilis and Paenibacillus macerans ˂ Paenibacillus polymyxa ˂ Bacillus licheniformis ˂ Bacillus thuringiensis ˂ Bacillus megaterium ˂ Bacillus pumilis ˂ Bacillus cereus ˂ Paenibacillus circulans . Paenibacillus circulans and Bacillus cereus were identified as the most promising strains, biotransforming up to 48 percent of the total amount of hydrocarbons.

Qihang Liu, Li Pan, Huijing He et al.

Background: Studies about the combined effects of gaseous air pollutants and particulate matters are still rare. Objectives: This study was performed based on baseline survey of the Diverse Life-Course Cohort in the Beijing-Tianjin-Hebei (BTH) Region of North China to evaluate the association of long-term air pollutants with blood pressure and the combined effect of the air pollutants mixture among 32821 natural han population aged 20 years or above. Methods: Three-year average exposure to air pollutants (PM10, PM2.5, PM1, O3, SO2, NO2, and CO) and PM2.5 components [black carbon (BC), ammonium (NH4+), nitrate (NO3−), sulfate (SO42−), and organic matter (OM)] of residential areas were calculated based on well-validated models. Generalized linear mixed models (GLMMs) were used to estimate the associations of air pollutants exposure with the systolic blood pressure (SBP), diastolic blood pressure (DBP), Mean arterial pressure (MAP), pulse pressure (PP) and prevalent hypertension. Quantile g-Computation and Bayesian Kernel Machine Regression (BKMR) were employed to assess the combined effect of the air pollutant mixture. Results: We found that long-term exposures of O3, PM2.5, and PM2.5 components were stably and strongly associated with elevated SBP, DBP, and MAP and prevalent hypertension. O3 increased SBP, DBP, and MAP at a similar extent, but with greater effects; while, PM2.5 and PM2.5 components had a greater impact on SBP than DBP, which increased PP simultaneously. In multi-pollutant models, the combined effects of the air pollutant mixture on blood pressure and prevalent hypertension was predominantly influenced by O3, PM2.5, and O3, OM in different models, respectively. For example, O3, PM2.5 contributed 57.25 %, 39.22 % of the positive combined effect of the air pollutant mixture on SBP; and O3, OM positively contributed 70.00 %, 30.00 % on prevalent hypertension, respectively. There were interactions between O3, CO, SO2 and PM2.5 components on hbp, SBP and PP. Conclusions: The results showed positive associations of air pollutant mixtures with blood pressure, where O3 and PM2.5 (especially OM) might be primary contributors. There were interactions between gaseous air pollutants and PM2.5 components on blood pressure and prevalent hypertension.

Arthur Pilone, Paulo Meirelles, Fabio Kon et al.

A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support.

Dezhi Ran, Mengzhou Wu, Wei Yang et al.

By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs making FM crisis a tangible concern in the coming decade, appealing for new theories and methodologies from the field of software engineering. In this paper, we outline our vision of introducing Foundation Model (FM) engineering, a strategic response to the anticipated FM crisis with principled engineering methodologies. FM engineering aims to mitigate potential issues in FM development and application through the introduction of declarative, automated, and unified programming interfaces for both data and model management, reducing the complexities involved in working with FMs by providing a more structured and intuitive process for developers. Through the establishment of FM engineering, we aim to provide a robust, automated, and extensible framework that addresses the imminent challenges, and discovering new research opportunities for the software engineering field.

Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee et al.

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.

Rungroj Piyaphanuwat, Suwimol Asavapisit

This research investigated the effects of incorporating bentonite and zeolite into bottom blended clay liners (BBCLs) on their engineering properties and adsorption capacity. BBCLs composed of clay, water treatment residue, and calcium carbide in a 40:40:20 ratio by volume were investigated with the addition of 1, 2, and 3 wt.% bentonite or zeolite. The experimental results indicated that bentonite and zeolite had positive effects on the slake durability index and the equilibrium time for the adsorption of lead (Pb) and chromium (Cr) from the leachate, whereas the unconfined compressive strength (UCS) and coefficient of permeability were negatively affected compared to those of the samples without bentonite and zeolite. At 56 days, the UCS of BBCLs with bentonite and zeolite decreased from 3.4 to 2.2MPa, while the coefficient of permeability of all the samples met the regulatory limit of sanitary landfills, which was given at 1×10-7cm/s. The slake durability index of all samples was lower 50% but it remained higher compared to BBCLs without bentonite and zeolite (approximately doubled). Both additives enhanced the equilibrium time and percentage of Pb and Cr adsorption by about 20% and 200%, respectively. SEM-EDS results show the adsorption of Pb and Cr onto the raw materials, calcium silicate hydrate (CSH), zeolite, and bentonite. Therefore, the addition of zeolite can increase the ability to adsorb heavy metals form leachate, which is suitable for use in secured landfills.

Pavan Kumar, N. Sharma, Shubham Sharma et al.

Abstract The in-vitro meat is a novel concept in food biotechnology comprising field of tissue engineering and cellular agriculture. It involves production of edible biomass by in-vitro culture of stem cells harvested from the muscle of live animals by self-organizing or scaffolding methodology. It is considered as efficient, environmental friendly, better ensuring public safety and nutritional security, as well as ethical way of producing meat. Source of stem cells, media ingredients, supply of large size bioreactors, skilled manpower, sanitary requirements, production of products with similar sensory and textural attributes as of conventional meat, consumer acceptance, and proper set up of regulatory framework are challenges faced in commercialization and consumer acceptance of in-vitro meat. To realize any perceivable change in various socio-economic and environmental spheres, the technology should be commercialized and should be cost-effective as conventional meat and widely accepted among consumers. The new challenges of increasing demand of meat with the increasing population could be fulfill by the establishment of in-vitro meat production at large scale and its popularization. The adoption of in-vitro meat production at an industrial scale will lead to self-sufficiency in the developed world.

GAO Lan, MAO Huiqin, LU Xi*

Obtaining accurate emissions of methane (CH_4), one of the most important non-carbon-dioxide greenhouse gases, is the basis for formulating and validating emission reduction policies. In terms of shortcomings from the "bottom-up" approach, this study combined the vehicle-based monitoring and the AERMOD atmospheric dispersion modeling system to derive the emission rates and emission factors of main CH_4 sources in one demonstration coal mine in Jincheng city, Shanxi province. After systematically considering the topography, meteorological conditions, and infrastructure distribution of the coal mine, both the mobile and downwind stationary monitoring alternatives were adopted, using a platform equipped with a high-precision greenhouse gas analyzer. Results showed that the simulated CH_4 emission rate of a single ventilation shaft under non-production condition (763 kg/h) was about 15.9% lower than the data provided by the enterprise in production. If ignoring the fugitive emissions, the derived CH_4 emission factor of the coal mine was 15.09 m^3/t, which was 13.8% smaller than that in " bottom-up" inventory, indicating that the working conditions of the coal mine played a large role in CH_4 emissions. One ventilation shaft and two vent stacks in the gas gathering station were the main point sources, and six coal silos were the fugitive sources, the emission factors of which were 8.6 m^3/t( 43%), 6.49 m^3/t (33%) and 4.87 m^3/t (24%), respectively. The traditional "bottom-up" accounting without consideration of fugitive emissions, resulted in a nearly 24% under estimation of CH_4 emissions even under non-production conditions, which could be compensated by the methane quantification method based on vehicle-based monitoring.

Heechan Han, Boran Kim, Kyunghun Kim et al.

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle to improving the accuracy and efficiency of hydrological analysis. To address this issue, we developed a machine learning algorithm-based precipitation data recovery tool to detect and predict missing precipitation data at observatories. This study investigated 30 weather stations in South Korea, evaluating the applicability of machine learning algorithms (artificial neural network and random forest) for precipitation data recovery using environmental variables, such as air pressure, temperature, humidity, and wind speed. The proposed model showed a high performance in detecting the missing precipitation occurrence with an accuracy of 80%. In addition, the prediction results from the models showed predictive ability with a correlation coefficient ranging from 0.5 to 0.7 and R2 values of 0.53. Although both algorithms performed similarly in estimating precipitation, ANN performed slightly better. Based on the results of this study, we expect that the machine learning algorithms can contribute to improving hydrological modeling performance by recovering missing precipitation data at observation stations. HIGHLIGHTS Missing precipitation data is recovered using ANN and RF algorithms.; Air humidity and air pressure have a high correlation with precipitation occurrence.; Both models have high performance in detecting the precipitation occurrence.; ANN model has better performance than the RF model for recovering daily precipitation data in South Korea.;