Indoor Environments最新文献

{"title":"An indoor air pollution research strategy for the United Arab Emirates","authors":"Jacqueline MacDonald Gibson ,&nbsp;Rahaf Ajaj ,&nbsp;Ahmed Al Khazraji ,&nbsp;Latifa Al Shamsi ,&nbsp;William P. Bahnfleth ,&nbsp;Daniel Bonn ,&nbsp;Katherine Bronstein ,&nbsp;Rania Dghaim ,&nbsp;Taher Eldanaf ,&nbsp;Mohamed El Sadig ,&nbsp;Moshood Olawale Fadeyi ,&nbsp;Diana Francis ,&nbsp;Grace Kilroy ,&nbsp;Samrin Ahmed Kusum ,&nbsp;Yuguo Li ,&nbsp;Amal Mubarak Madhi ,&nbsp;Mily Mathew ,&nbsp;Shelly L. Miller ,&nbsp;Jordan Peccia ,&nbsp;Driss Samri ,&nbsp;Fares H. Zaitoun MD","doi":"10.1016/j.indenv.2025.100086","DOIUrl":"10.1016/j.indenv.2025.100086","url":null,"abstract":"<div><div>Clean indoor air is vital for health in all settings, especially in locations where extreme climates restrict outdoor activity, such as in the United Arab Emirates (UAE). UAE summer temperatures routinely exceed 42°C (108°F), discouraging outdoor activity and limiting natural ventilation of structures. Yet, little research is available on indoor air quality in the UAE. To inform the design of a new indoor air quality program, the Abu Dhabi Public Health Centre commissioned a study to characterize and prioritize knowledge gaps on indoor air quality and its relationship to health in the UAE and to identify potential partners for the program. Research gaps and priorities were identified by a panel of 16 international and local indoor air quality experts through a two-day structured, in-person workshop and follow-up survey. Key partners were identified through a stakeholder mapping exercise and e-mail survey of 79 government agencies and nongovernment organizations. The expert panel concluded that the most important short-term research need is to characterize the major sources of indoor air pollution and the most frequently occurring pollutants. The panel recommended establishing a national indoor air quality observatory encompassing a wide range of settings, including residences, schools, mosques, healthcare facilities, shopping malls, and other public spaces. Indoor air quality monitors would be permanently placed to establish baseline indoor air quality, provide data to estimate source contributions, and enable tracking of changes over time. The stakeholder mapping exercise identified ten agencies that should be involved in planning, including the Abu Dhabi Public Health Centre, Department of Health–Abu Dhabi, Environment Agency–Abu Dhabi, Abu Dhabi Department of Energy, and Emirates Public Health Association. While focused on the UAE, the methods and research priorities in this study may be useful for planning indoor air quality improvement campaigns in other high-income nations.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 2","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indoor laundry drying: Full-scale determination of water emission rate and impact on thermal comfort","authors":"Frédéric Thevenet ,&nbsp;Florent Caron ,&nbsp;Vincent Gaudion ,&nbsp;Mélanie Nicolas ,&nbsp;Marie Verriele","doi":"10.1016/j.indenv.2025.100089","DOIUrl":"10.1016/j.indenv.2025.100089","url":null,"abstract":"<div><div>To save energy, passive drying is encouraged by environmental agencies. Depending on seasons, climates and dwellings, laundry drying is performed indoors. Passive laundry drying is an indoor practice with high cultural variability. This work provides real-scale experimental data on water transfer to indoor environment and thermal impacts of laundry drying on rack. Representative laundries and drying practices are defined to validate a full-scale experimental methodology. Laundry and drying scenarios are explored using the 40-m³ experimental room IRINA. Among typical cotton and polyester laundries, cotton is selected as clothing of interest, with ca. 2000 g of water to be evaporated for specific laundry scenario. The simultaneous monitoring of water mass loss from laundry rack and humidity over the drying period allows for determination of water emission rate from laundry. During the first 2-hours of drying, water emission rate exceeds 100 g h⁻¹ (maximum 360 g h⁻¹.) Three different emission regimes over time are discussed in terms of water concentration gradient at the air and laundry interface. Consequences on indoor temperature are quantitated over drying. Initial relative humidity of indoor environment impacts the kinetics of water transfer and drives the thermal impacts: temperature drops from 0.5 to 3.8 °C are recorded. Based on these full-scale hygrothermal data, the impact of laundry drying on indoor thermal comfort is discussed. Two indoor comfort scenarios allow for assessing the magnitude of the impact of laundry drying. This work provides full-scale methodology with hygrothermal experimental datasets and a new insight on an impactful indoor practice.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 2","pages":"Article 100089"},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating statistical methods to predict indoor black carbon in an urban birth cohort","authors":"Sherry WeMott ,&nbsp;Grace Kuiper ,&nbsp;Sheena E. Martenies ,&nbsp;Matthew D. Koslovsky ,&nbsp;William B. Allshouse ,&nbsp;John L. Adgate ,&nbsp;Anne P. Starling ,&nbsp;Dana Dabelea ,&nbsp;Sheryl Magzamen","doi":"10.1016/j.indenv.2025.100084","DOIUrl":"10.1016/j.indenv.2025.100084","url":null,"abstract":"<div><div>Most air pollution epidemiology studies rely on outdoor exposure data from various sources, such as reference monitors, low-cost monitors, models, or Earth observations. However, people spend 90 % of their time indoors, with 70 % of that time spent at home, which may result in misclassification of air pollution exposure when using data reflecting ambient concentrations. In this study, we evaluated methods to predict residential indoor black carbon (BC) from outdoor BC, PM2.5, and housing characteristics to support future efforts in estimating personal air pollution exposure. Households from the Healthy Start cohort in Denver, CO hosted paired indoor/outdoor low-cost air samplers for one-week periods during spring 2018, summer 2018, and winter 2019. Participants completed questionnaires about housing characteristics like building type, flooring, and heating and cooling methods. Filters were analyzed for BC using transmissometry. Ridge, LASSO and ordinary least squares regression (OLS) techniques were used to build predictive models of indoor BC given the available set of covariates. Leave-one-out cross-validation was used to assess the predictive accuracy of each model. We hypothesized that Ridge and LASSO will obtain improved predictive performance over the OLS model due to regularization. A total of 27 households participated, with 39 paired measurements available after data cleaning. All winter data were excluded due to high variability and incomplete sampling times for outdoor measurements. Performance issues suggested insufficient weatherproofing of monitors for low temperatures. The Ridge regression showed the best predictive performance. The final inference model included outdoor PM_2.5, hard floors, and the presence of pets in the home, accounting for approximately 28 % of the variability in indoor BC concentrations measured in participant homes. In the absence of indoor monitoring, household characteristics like flooring and the presence of pets can help predict indoor levels of BC.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 2","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating air exchange rates in thousands of elementary school classrooms using commercial CO2 sensors and machine learning","authors":"Yirong Yuan ,&nbsp;Masanao Yajima ,&nbsp;Jinho Lee ,&nbsp;Katherine H. Walsh ,&nbsp;Brenden Tong ,&nbsp;Lauren Main ,&nbsp;Lauren Bolton ,&nbsp;M. Patricia Fabian","doi":"10.1016/j.indenv.2025.100083","DOIUrl":"10.1016/j.indenv.2025.100083","url":null,"abstract":"<div><div>In the post-COVID-19 pandemic era, maintaining clean air in school classrooms has become critical for ensuring student health and safety. Air exchange rate (AER), which measures the number of air replacements in a room per hour, is a standard metric for assessing ventilation, with recommended targets provided by organizations worldwide. Installing comprehensive carbon dioxide (CO₂) monitoring in schools has expanded opportunities for automating AER estimation, but most schools have limited computational resources and analytical capacity. To address this, we developed a cost-effective and scalable method to estimate AER by leveraging end-of-school day carbon dioxide concentrations recorded with thousands of commercial sensors in classrooms. This method assumes well-mixed conditions and replicates the tracer gas technique, leveraging statistical machine learning and knowledge of classroom operations to automate AER calculations at the end of occupied periods. We analyzed data from 3206 sensors across 125 schools in a large urban school district in the Northeastern United States and identified 648,956 CO₂ decay curves over one school year. After applying data screening criteria, we calculated 323,776 AER values, averaging 84 values (SD = 40) per classroom. Calculated AER ranged from &lt; 0.1–64 h⁻¹, averaging 3.0 h⁻¹ (SD = 2.9). The average AER in schools with central mechanical ventilation was 1.8 times higher than in schools without mechanical ventilation. The method is optimized to use parallel and high-performance computing resources, and calculates daily air exchange rates for an entire classroom over an academic school year in a few seconds, an entire school in a few minutes, and the entire school district in a few hours. To our knowledge, this is the largest deployment of commercial CO₂ sensors in schools that publicly share data. The AER calculation method is scalable and efficient, and automates cleaning, selection, and processing of CO₂ data from commercial sensors, with methods and code transferable to other schools collecting similar large-scale data.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 2","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure of children and adolescents to volatile organic compounds in indoor air: Results from the German Environmental Survey 2014–2017 (GerES V)","authors":"Annika Fernandez Lahore ,&nbsp;Robert Bethke ,&nbsp;Anja Daniels ,&nbsp;Konrad Neumann ,&nbsp;Stefan Ackermann ,&nbsp;Nadine Schechner ,&nbsp;Klaus-Reinhardt Brenske ,&nbsp;Enrico Rucic ,&nbsp;Aline Murawski ,&nbsp;Marike Kolossa-Gehring ,&nbsp;Wolfram Birmili","doi":"10.1016/j.indenv.2025.100082","DOIUrl":"10.1016/j.indenv.2025.100082","url":null,"abstract":"<div><div>Indoor air concentrations of volatile organic compounds (VOC) were determined in the living environments of German children and adolescents between 2014 and 2017 during the German Environmental Survey (GerES) V. Passive sampling on Tenax TA tubes over 7 days and subsequent thermodesorption/gas chromatographic analysis yielded concentrations of 61 compounds from the groups of alcohols, alkanes, aromatics, carboxylic acid esters, glycol ethers, halogenated hydrocarbons, siloxanes, and terpenes as well as a value for total VOC (TVOC). The most abundant single compounds were decamethylcyclopentasiloxane (D5), limonene, α-pinene, butyl acetate, toluene, and 2-ethylhexanol, with geometric mean concentrations ranging between 12 and 4.5 µg/m³ . The guideline values established by the German Committee on Indoor Guidance Values (AIR) were exceeded in less than 1.3 % of participants when considered as a sum parameter for cyclic dimethylsiloxanes, monocyclic monoterpenes, bicyclic terpenes, C9-C14 alkanes, and xylenes. The concentrations of most compounds were lower in GerES V (2014/17) compared to the previous cycle GerES IV (2003/06). The concentrations of individual compounds showed moderate associations with exposure factors as identified from questionnaire data including the socio-economic status of the household, migration background, smoking status, the presence of wooden furniture, renovations in the test room, the age of the house, outdoor pollution (proximity to road traffic), as well as the age and sex of the participants.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 1","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal effects estimation: Using natural experiments in observational field studies in building science","authors":"Ruiji Sun ,&nbsp;Stefano Schiavon ,&nbsp;Gail Brager ,&nbsp;Haiyan Yan ,&nbsp;Thomas Parkinson","doi":"10.1016/j.indenv.2025.100080","DOIUrl":"10.1016/j.indenv.2025.100080","url":null,"abstract":"<div><div>Correlational analysis, such as linear regression, does not imply causation. This paper introduces and applies a causal inference framework and a specific method, regression discontinuity, to thermal comfort field studies. The method utilizes policy thresholds in China, where the winter district heating policy is based on cities' geographical locations relative to the Huai River. The approximate latitude of the Huai River can be considered as a natural, geographical threshold, where cities near the threshold are quite similar, except for the availability of district heating in cities north of the threshold, creating a situation similar to a randomized experiment. Using the regression discontinuity method, we quantify the causal effects of the experiment treatment (district heating) on the physical indoor environments and subjective responses of building occupants. We found that mean indoor operative temperatures were 4.3 °C higher, and mean thermal sensation votes were 0.6 warmer due to the district heating. In contrast, using conventional correlational analysis, we demonstrate that the correlation between indoor operative temperature and thermal sensation votes does not accurately reflect the causal relationship between the two. We also show that the indoor operative temperature could be either positively or negatively correlated with occupants’ thermal satisfaction. However, we cannot conclude that increasing the indoor operative temperature in these circumstances will necessarily lead to higher or lower thermal satisfaction. This highlights the importance of causal inference methods in thermal comfort field studies and other observational studies in building science, where the regression discontinuity method might apply.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 1","pages":"Article 100080"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demonstration of a novel tracer gas method to investigate indoor air mixing and movement","authors":"Chai Yoon Um ,&nbsp;William W. Delp ,&nbsp;Rowan C. Blacklock ,&nbsp;Brett C. Singer","doi":"10.1016/j.indenv.2025.100081","DOIUrl":"10.1016/j.indenv.2025.100081","url":null,"abstract":"<div><div>This paper reports on equipment and procedures that enable the application of the pulsed tracer method to study air movement, contaminant transport, and mixing in rooms. We use ethanol as a non-toxic tracer and a network of low-cost, fast response (2 s) metal oxide sensors to measure airborne concentrations at high frequency. The method was demonstrated in a 158 m³ room of the FLEXLAB facility at Lawrence Berkeley National Laboratory, with an overhead HVAC system with controllable supply airflow and temperature. The room was configured as a meeting space with 8 simulated occupants. The sensors were mounted in a 3 × 4 grid in the upper room (0.3 m from the 2.74 m ceiling), in the middle height of the room at 1.1–1.4 m, and at several locations 0.1–0.4 m from the floor. Vaporized ethanol was released in pulses of 20 s. Sensors were cross-calibrated in-situ to provide quantitative information about relative concentrations and exposures. Results show that the method provides quantitative information about air movement patterns and mixing. For example, mixing throughout the room took 3–4 min with high supply airflow at neutral temperature and 7.5–9 min with heated supply air provided at a lower rate. The test can be used to evaluate whether air movement from the occupied zone to the upper room is fast enough to achieve the extremely high air cleaning rates that are possible with upper room germicidal ultraviolet disinfection (GUV) systems under ideal mixing conditions.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 1","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Ventilation characteristics in a hospital where a COVID-19 outbreak occurred in the winter of 2020” [Indoor Environ. 2 (2025) 100065]","authors":"Motoya Hayashi ,&nbsp;Sayaka Murata ,&nbsp;Koki Kikuta","doi":"10.1016/j.indenv.2025.100076","DOIUrl":"10.1016/j.indenv.2025.100076","url":null,"abstract":"","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 1","pages":"Article 100076"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing Bayesian inference on a stochastic CO2-based grey-box model","authors":"Shujie Yan ,&nbsp;Jiwei Zou ,&nbsp;Chang Shu ,&nbsp;Justin Berquist ,&nbsp;Vincent Brochu ,&nbsp;Marc Veillette ,&nbsp;Danlin Hou ,&nbsp;Caroline Duchaine ,&nbsp;Liang (Grace) Zhou ,&nbsp;Zhiqiang (John) Zhai ,&nbsp;Liangzhu (Leon) Wang","doi":"10.1016/j.indenv.2025.100079","DOIUrl":"10.1016/j.indenv.2025.100079","url":null,"abstract":"<div><div>The COVID-19 pandemic brought global attention to indoor air quality (IAQ), which increases public’s awareness on monitoring indoor ventilation conditions significantly. Indoor CO₂ monitoring has been widely accepted as an effective way for indicating IAQ conditions, attributed to its close relationships with indoor air change rates. However, real-time estimation of air change rates or CO₂ emission rates from CO₂ measurement data remains challenging due to uncertainties in factors like random air movements, dynamic conditions (e.g., weather and occupancy), and the limitations of deterministic equations. This study addresses these challenges by applying Bayesian inference to a stochastic CO₂-based grey-box model, enabling the accurate estimation of ventilation and CO₂ emission rates while accounting for uncertainty. The model’s accuracy and robustness were validated through CO₂ tracer gas experiments, employing constant injection and decay methods in a large-scale aerosol chamber. Both prior and posterior predictive checks (PPC) were performed to verify this approach. The approach proposed by this study improves the interpretation of CO₂ monitoring data, thereby facilitating the future real-time IAQ management.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 1","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CO2 levels and SARS-CoV-2 transmission in public schools: A retrospective cohort study in Montreal (Canada)","authors":"Olivier Séguin ,&nbsp;Dorra Ghorbal ,&nbsp;Gabrielle Denault ,&nbsp;Karine Trudeau ,&nbsp;Patrick Poulin ,&nbsp;Catherine Dea ,&nbsp;Stéphane Perron","doi":"10.1016/j.indenv.2025.100077","DOIUrl":"10.1016/j.indenv.2025.100077","url":null,"abstract":"<div><div>Scientific evidence suggests that SARS-CoV-2 is transmitted primarily through close contact with susceptible individuals and that the risk of transmission increases during prolonged exposure in confined, inadequately ventilated and densely occupied spaces. In response to concerns related to inadequate ventilation for students and staff, CO₂ concentrations were measured in all of Montreal’s public elementary and high schools during winter of 2020–21. This study aims to evaluate the associations between ventilation system types, mid-class CO₂ concentrations and SARS-CoV-2 transmission amongst Montreal's public school’s attendees during the 2020–2021 school year. Data on building ventilation types, CO₂ measurements and school characteristics such as total enrollment and socioeconomic status (SES), were sourced from Ministry of Education’s administrative data for the 2020–2021 school year. During this pandemic period, the Montreal Public Health Department investigated COVID-19 cases among students and staff in public elementary and high schools, gradually developing a regional database of school cases and outbreaks from intervention files. Negative binomial regression models were employed to examine associations between mid-class CO₂ concentrations and COVID-19 incidence rates for both total cases and those acquired within schools. Regression models were adjusted for school types, neighborhood COVID-19 incidence rates and school SES. The mid-class CO₂ concentration<del>s</del> median was 1050 ppm in the 384 school buildings. CO₂ concentrations were associated with ventilation systems, with schools using natural ventilation exhibiting higher concentrations compared to three other types of mechanical ventilation. No positive association was observed between the incidence rates of school-acquired cases and higher mid-class CO₂ concentrations in both unadjusted and adjusted models.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"2 1","pages":"Article 100077"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}