Indoor air最新文献

{"title":"Machine Learning–Based Prediction of Occupant Symptom Risk Using Indoor and Outdoor Environmental Quality Data in University Dormitories","authors":"Yong Yu,&nbsp;Marco Gola,&nbsp;Gaetano Settimo,&nbsp;Stefano Capolongo","doi":"10.1155/ina/8230467","DOIUrl":"https://doi.org/10.1155/ina/8230467","url":null,"abstract":"<p>Indoor air quality (IAQ) is a major determinant of occupant health, with people spending over 90% of their time indoors. This study presents a machine learning–based classification framework for predicting self-reported health symptoms in university dormitory residents, using continuous indoor and outdoor environmental monitoring data and occupant survey responses collected across 7 monitoring periods from May 2024 to June 2025 in 2 student accommodation facilities in Milan, Italy. From 74 occupant samples, 131 candidate predictors were derived from 5 indoor and 5 outdoor environmental parameters together with occupant-level and contextual variables and reduced to symptom-specific feature sets of 3–40 predictors through permutation importance. Seven symptoms each reported by at least 10 occupants (sneezing, headache, cough, dry skin, fatigue, dry/sore throat and runny nose) were modelled as independent binary classification tasks under leave-one-period-out cross-validation (LOPO-CV), with the best classifier selected from 23 candidates per symptom based on balanced accuracy and thresholds set individually by Youden′s J index. Mean AUC across the 7 models was 0.595 (range: 0.398–0.705), with 4 symptoms exceeding AUC 0.6; dominant predictive signals were drawn from outdoor environmental statistics and distributional shape features rather than mean indoor pollutant concentrations. At a combined accuracy criterion of ≥ 5/7 symptoms correctly classified, 58.1% of occupants were correctly profiled across all 74 samples, rising to 64.3% in the symptomatic subgroup (<i>n</i> = 42); excluding the 2 symptoms with the lowest model specificity raised ≥ 3/5 combined accuracy to 92.0% across the full sample and 90.0% in the symptomatic subgroup. The results demonstrate the feasibility of multisymptom environmental risk profiling and support the concept of a learning building, in which continuously updated occupant health models can inform proactive and personalised indoor environment management.</p>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/8230467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A SynAir-G Position Statement for the Improvement of Indoor Air Quality for Children","authors":"Kristina Siemens,&nbsp;Tamar Abramidze,&nbsp;Cezmi Akdis,&nbsp;Mübeccel Akdis,&nbsp;Isabella Annesi Maesano,&nbsp;Nikos Antoniou,&nbsp;Stathis Antoniou,&nbsp;Sylvie Broussous,&nbsp;Davide Caimmi,&nbsp;Yash Chadha,&nbsp;Panagiotis-Minos Chaslaridis,&nbsp;Athanasios Charalampopoulos,&nbsp;Athanasios Damialis,&nbsp;Sabato D′Auria,&nbsp;Justine De Leus,&nbsp;Pascal Demoly,&nbsp;Eleni Dovrou,&nbsp;Varpu Elenius,&nbsp;Guisy Esposito,&nbsp;Liz Fay,&nbsp;Ioanna Fergadiotou,&nbsp;Gabriel Filios,&nbsp;Minas Gadalla,&nbsp;Kyriakos Giannopoulos,&nbsp;Alessandro Giusti,&nbsp;Christian Goroncy,&nbsp;Maia Gotua,&nbsp;Pierre Guitton,&nbsp;Abdolrasoul Habibipour,&nbsp;Antonis Hadjiantonis,&nbsp;Maria Hartiala,&nbsp;Ingrid van Hofman,&nbsp;Hanane Idrissi,&nbsp;Heidi Jartti,&nbsp;Olli Jartti,&nbsp;Tuomas Jartti,&nbsp;Hajar Khafifi,&nbsp;Nouhaila Khalif,&nbsp;George Kontogiannis,&nbsp;Makis Koulombis,&nbsp;Maria Kritikou,&nbsp;Nicolas Kyriakou,&nbsp;Pascal Larigaldie,&nbsp;Konstantinos Loupos,&nbsp;Clare Murray,&nbsp;Antonis Myrgiakis,&nbsp;Thanos Nenes,&nbsp;Susetta Finotto,&nbsp;Sotiris Nikoletseas,&nbsp;Spyros N. Pandis,&nbsp;Andreas Papadopoulos,&nbsp;Eleni-Maria Papatesta,&nbsp;Agata Papotto,&nbsp;Yagiz Pat,&nbsp;Markos Psimitis,&nbsp;Valeria Ramiconi,&nbsp;Marco Randon,&nbsp;Christoforos Raptopoulos,&nbsp;Maia Rukhadze,&nbsp;Angela Simpson,&nbsp;Martti Siniharju,&nbsp;Maria Staiano,&nbsp;Stella Taka,&nbsp;Luciana Kase Tanno,&nbsp;Pantelis Tzamalis,&nbsp;Antonio Varriale,&nbsp;Paraskevi Xepapadaki,&nbsp;Torsten Zuberbier,&nbsp;Nikolaos G. Papadopoulos","doi":"10.1155/ina/9690417","DOIUrl":"https://doi.org/10.1155/ina/9690417","url":null,"abstract":"<p>Air pollution has been recognised as the single biggest environmental threat to human health within the European Union (EU). Indoor air pollution has received less attention than outdoor air pollution, even though we spend most of our time indoors. It is associated with a variety of short- and long-term health effects. These include allergies and asthma with a key role for the exposure to bioaerosols and synergistic interactions between chemical and biological pollutants. Given the dramatic rise of allergic diseases, the prevention of symptom exacerbations and the new onset of disease are of great importance, particularly for vulnerable populations such as children. The lack of a unified EU-wide approach involving all stakeholders represents a major unmet need. The EU-funded IDEAL Cluster comprises seven research projects to advance knowledge on indoor air quality (IAQ) and its impact on health, inform policies and thus promote healthier indoor environments. The ‘SynAir-G’ project forms part of this cluster and was launched in 2022 with the participation of 18 partners from 11 countries. It focuses on schools to explore synergistic interactions between chemical and biological pollutants based on the hypothesis that these modulate major health effects. SynAir-G includes the development of low-cost sensors, collection of health outcomes, modelling of pollution dynamics and the development of mitigation solutions. The knowledge will be circulated to all stakeholders and the public aiming to establish standards and health-based guidelines. The scope of this position statement is (1) to review the current evidence on indoor air pollution, its impact on health and particularly on children with allergies and asthma; (2) to give an overview of the European regulatory framework; (3) to describe knowledge gaps, challenges and unmet needs; and (4) to present SynAir-G′s project goals and propose a roadmap to improve IAQ for children with asthma and allergies.</p>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/9690417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Residential Incense Burning, Indoor Air Pollution, and Cardiovascular Hemodynamic Alterations","authors":"Jia-Kun Chen,&nbsp;Chi-Hua Cheng,&nbsp;Yun-Shan Chung,&nbsp;Zhi-Yu Liu,&nbsp;Ci-Wen Luo,&nbsp;Ta-Chen Su","doi":"10.1155/ina/8778576","DOIUrl":"https://doi.org/10.1155/ina/8778576","url":null,"abstract":"<p>Indoor incense burning is an important yet underrecognized source of indoor air pollution. This study examined short-term associations between residential air pollutants, ventilation conditions, and cardiovascular hemodynamics. Seventy-three adults from the YOung TAiwanese (YOTA) cohort were recruited from 60 households. Twenty-four-hour indoor particulate matter (PM_2.5 and PM₁₀) and gaseous pollutants (CO, CO₂, TVOCs, and formaldehyde) were continuously monitored alongside noninvasive cardiovascular hemodynamic assessments. Incense-burning households exhibited significantly higher pollutant concentrations and worse cardiovascular parameters, including higher blood pressure, cardiac workload, and lower vascular compliance. In mixed-effects models, gaseous pollutants (especially CO, CO₂, and TVOCs) were consistently associated with adverse hemodynamic changes. Crucially, interaction analyses revealed strong possible combined pollutant effects between PM_2.5 and gaseous pollutants (TVOCs and CO), which profoundly exacerbated myocardial contractility and severely impaired vascular elasticity. Furthermore, in non–incense-burning environments, poor ventilation (indicated by elevated CO₂) acted as an independent physiological stressor, triggering acute compensatory hemodynamic responses. These findings indicate that short-term residential exposure to indoor air pollutants, especially as complex combustion mixtures, acutely alters cardiovascular hemodynamics. Beyond single pollutants, evaluating synergistic interactions and ventilation efficiency is critical for understanding cardiovascular stress. Ultimately, improving indoor ventilation is a vital strategy for mitigating cardiovascular risks.</p>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/8778576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Informed MTGODE for Indoor CO2 Concentration Prediction via Integration of Mass Conservation Law","authors":"Yuhan Hu,&nbsp;Suli Zhang,&nbsp;Yanjie Han","doi":"10.1155/ina/2063106","DOIUrl":"https://doi.org/10.1155/ina/2063106","url":null,"abstract":"<p>Accurate prediction of indoor CO₂ concentration is essential for maintaining healthy indoor environments and enabling intelligent ventilation control. Existing data-driven methods mainly rely on discrete-time modeling, which limits their ability to characterize the continuous diffusion dynamics of indoor pollutants. Moreover, the absence of physical constraints often leads to cumulative errors during long-term forecasting, especially under data-scarce conditions. To address these challenges, this paper proposes a physics-informed multivariate time-series graph neural ordinary differential equation (PI-MTGODE) model. The proposed framework captures spatial dependencies among multiple monitoring nodes through adaptive graph learning and models continuous-time environmental evolution using Neural ODEs. Furthermore, a derivative-space physical coupling mechanism is introduced to embed the CO₂ mass conservation law into the Neural ODE vector field, improving the dynamical consistency of long-term predictions through structural physical constraints. By treating the ventilation decay coefficient <i>k</i> and comprehensive source term <i>f</i> as learnable physical parameters, the proposed model enables unsupervised identification of indoor environmental dynamics. Experimental results show that, compared with the baseline MTGODE, PI-MTGODE reduces RMSE by 29.7% and 11.7% and decreases the mass conservation residual (MCR) by 39.5% and 66.1% on the evaluated datasets. Under sparse-data conditions with only 100–400 training samples, the proposed model maintains stable prediction performance and derives physically meaningful parameters consistent with indoor environmental principles. Overall, PI-MTGODE achieves a balance between prediction accuracy and physical consistency, providing an interpretable modeling framework for indoor air quality prediction and intelligent ventilation control in data-limited scenarios.</p>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/2063106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Household Clean Cooking Fuel Transition and Attenuated Cardiopulmonary Function Decline: A National Longitudinal Study of Middle-Aged and Older Adults in China","authors":"Qianfei Liu,&nbsp;Ling Hou,&nbsp;Huiling Li,&nbsp;Shaoxi Wang,&nbsp;Siqiao Liang,&nbsp;Zhiyi He,&nbsp;Quanfang Chen","doi":"10.1155/ina/8898458","DOIUrl":"https://doi.org/10.1155/ina/8898458","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The use of solid fuels for cooking is a significant public health concern, especially for middle-aged and older adults. Limited longitudinal data exists on the impact of transitioning to clean fuels on cardiopulmonary health.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Data from the China Health and Retirement Longitudinal Study (CHARLS) (2011–2020) were analyzed. From the baseline solid fuel users, we constructed two independent subcohorts to evaluate short-term (2013–2015) physiological changes and long-term (through 2020) disease risks, respectively. Multivariable regression models assessed short-term changes in peak expiratory flow (PEF), blood pressure, and pulse and long-term risks of cardiopulmonary diseases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among the 6534 participants in the short-term cohort, 27.4% transitioned to clean cooking fuels by 2013. The transition to clean fuels was associated with a significant attenuation of PEF decline by 11.0 L/min (95% CI: 4.7–17.3; <i>p</i> &lt; 0.001), which was partially mediated by reductions in depressive symptoms (mediating proportion = 12<i>%</i>). A modest but significant reduction in systolic blood pressure was also observed (<i>β</i> = −1.7, <i>p</i> = 0.017). In the long-term cohort (<i>n</i> = 6362), with 33.5% transitioning by 2015, clean fuel use was associated with a 20% lower risk of chronic lung disease (OR = 0.80, 95% CI: 0.7–1.0, <i>p</i> = 0.044) and heart disease (OR = 0.80, 95% CI: 0.7–1.0, <i>p</i> = 0.028) but no significant association with hypertension. Protective effects were more pronounced in nonsmokers and nondrinkers (OR range: 0.7–0.8).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Transition to clean cooking fuels is associated with slower pulmonary decline and lower cardiopulmonary disease risk, supporting its role in healthy aging strategies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/8898458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic and Predictive Modeling of Indoor CO2 Concentration and Its Impact on Energy Consumption in Educational Buildings","authors":"Ali Maboudi Reveshti,&nbsp;Rasool Maroofiazar","doi":"10.1155/ina/4441388","DOIUrl":"https://doi.org/10.1155/ina/4441388","url":null,"abstract":"<p>Indoor CO₂ concentration in educational buildings serves as both an indicator of ventilation adequacy and a direct driver of HVAC energy demand. Yet, these two dimensions are rarely examined within a single integrated framework. This study proposes such a framework, combining four coupled components: a whole-building EnergyPlus thermal model of a Mechanical Engineering faculty building (benchmarked against published data rather than calibrated with measured energy data), a physics-based mass balance CO₂ model driven by hourly occupancy and ventilation outputs from EnergyPlus, a long short-term memory (LSTM) network for predictive CO₂ forecasting, and a systematic six-category multiscenario energy analysis. The mass balance model reproduces peak classroom CO₂ concentrations of 1200–1300 ppm during full occupancy, exceeding the ASHRAE 1000-ppm threshold, and recovers to near-outdoor levels within 20–40 min after occupancy ends. The LSTM model delivers 1-h-ahead CO₂ forecasts with RMSE = 42 ppm and <i>R</i>² = 0.94, outperforming persistence (<i>R</i>² = 0.71), linear regression (<i>R</i>² = 0.88), and gradient boosting (<i>R</i>² = 0.92) baselines, with its largest advantage occurring during occupancy transitions. The six-category energy scenario analysis identifies HVAC schedule optimization (17%–22% annual savings), advanced double-pane LowE/LowER glazing (~10%), and reduced window area (~7%) as the most impactful interventions. Under the idealized IdealLoadsAirSystem representation, the fully optimized configuration cuts annual building energy consumption by 34% (30% in winter, 40% in summer), with ±5–7 percentage points of combined uncertainty; real HVAC systems would likely realize smaller savings due to equipment efficiency and control dynamics. Overall, the framework establishes a conceptual and methodological basis for future LSTM-driven demand-controlled ventilation (DCV) strategies in educational buildings. However, no closed-loop control algorithm was implemented or tested here.</p>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/4441388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A CFD-Based Quantitative Prediction Model for Air Change Rate and Particulate Matter Concentration in a Dynamically Occupied Class 10,000 Operating Room: A Case Study in Dalian, China","authors":"Yu Zhao,&nbsp;Kangning Wang,&nbsp;Yuhan Wang,&nbsp;Saihang Hu,&nbsp;Tareq Hussein,&nbsp;Zhiwen Luo,&nbsp;Hong Zheng","doi":"10.1155/ina/5838379","DOIUrl":"https://doi.org/10.1155/ina/5838379","url":null,"abstract":"<p>Maintaining a healthy indoor environment in operating rooms (ORs) is critical for protecting both patients and medical staff from airborne particulate matter (PM) exposure. However, current ventilation standards typically prescribe fixed air change rates (ACH) based on static or as-built conditions, which fail to account for dynamic occupancy variations. This study is aimed at establishing a quantitative relationship between ACH and PM concentration under dynamic conditions while ensuring indoor air quality in ORs. A Class 10,000 OR, classified according to the Chinese National Standard <i>GB 50333-2013</i>, was investigated by integrating field measurements with computational fluid dynamics (CFD) simulations. An orthogonal experimental design was employed to generate 45 representative operating scenarios. Sobol sensitivity analysis was applied to identify key influencing factors. Bayesian optimization (BO), multiobjective optimization (MOP), and response surface methodology (RSM) were further used to optimize and calibrate the nonuniformity coefficient (<i>ψ</i>) and a quantitative prediction model was subsequently established, linking ACH, number of personnel, and PM concentration. The results showed that the RSM model had lower prediction errors, with a root mean square error (RMSE) of 0.032, mean absolute error (MAE) of 0.024, coefficient of variation (<i>C</i>_<i>v</i>) of 0.052, and standard deviation (<i>σ</i>) of 0.022. In contrast, the <i>C</i>_<i>v</i> and <i>σ</i> values of MOP were 0.005 and 0.002, respectively, which were the lowest among the three algorithms, indicating that MOP exhibited higher stability and robustness when handling coupled disturbances caused by ACH and personnel-generated PM. While satisfying the <i>GB 50333-2013</i> Class 10,000 cleanroom requirement (PM_≥0.5 ≤ 352 PC/L), the minimum required ACH for surgical teams of 5–9 personnel ranges from 9.2 to 16.6 h⁻¹, significantly lower than the 18 h⁻¹ recommended by current standards. It was also observed that surgical lights considerably disrupt airflow organization, thereby affecting local PM distribution. The findings provide a scientific basis for transitioning from fixed ventilation design toward dynamic control strategies while ensuring indoor air quality in ORs.</p>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/5838379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Species-Level Characterization of Bacterial Persistence and Pathogen-Associated Taxa in Humid Subtropical Residential Split-Type Air Conditioners Based on Dual Sampling Strategies and Full-Length 16S rRNA Sequencing”","authors":"","doi":"10.1155/ina/9815929","DOIUrl":"https://doi.org/10.1155/ina/9815929","url":null,"abstract":"<p>J-S. Chen, H-Y. Wu, T-C. Hung, et al., “Species-Level Characterization of Bacterial Persistence and Pathogen-Associated Taxa in Humid Subtropical Residential Split-Type Air Conditioners Based on Dual Sampling Strategies and Full-Length 16S rRNA Sequencing”, <i>Indoor Air</i>, 2026, 7941150, https://doi.org/10.1155/ina/7941150</p><p>In the published version of this article, the equal-contribution designation for the first authors was inadvertently omitted during the production process. The equal contribution statement should appear for Jung-Sheng Chen, Huan-Yi Wu, Tung-Che Hung, and Bing-Mu Hsu.</p><p><b>Author Contributions</b></p><p>Jung-Sheng Chen, Huan-Yi Wu, Tung-Che Hung, and Bing-Mu Hsu contributed equally to this work.</p><p>This correction does not affect the scientific content, results, interpretation, or conclusions of the article.</p><p>We apologize for this error.</p>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/9815929","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation Analysis of Indoor and Outdoor Climate Conditions and Development of a Representative Indoor Climate Model for Latvian Residential Buildings","authors":"Arturs Palcikovskis,&nbsp;Anatolijs Borodinecs,&nbsp;Stanislavs Gendelis","doi":"10.1155/ina/4454464","DOIUrl":"https://doi.org/10.1155/ina/4454464","url":null,"abstract":"<p>This article presents the development and approbation of an indoor climate model for Latvian residential buildings based on long-term measurements and statistical analysis. The study analyzes 2,195,785 records of indoor air temperature and humidity collected from 36 dwellings across seven Latvian cities between 2023 and 2025, with outdoor air temperature used as the key variable. Piecewise regression revealed a distinct breakpoint at an outdoor temperature of +7.86°C, below which indoor temperature remains nearly constant at approximately +21.7°C due to heating system operation, showing negligible correlation with outdoor conditions. Above this threshold, a moderate correlation was observed, indicating free-running indoor behavior. Indoor relative humidity and the indoor–outdoor absolute humidity difference were best described by simple linear regressions, exhibiting moderate positive and negative correlations with outdoor temperature, respectively. Comparison with European standards (EN and DIN) demonstrated substantial deviations, particularly during the heating season, where measured indoor temperatures were higher and moisture dynamics differed from standardized assumptions. Hygrothermal simulations using the DELPHIN program showed that the standard EN 15026 indoor climate underestimated mold risk (mold index VTT 1.5), while the developed Latvian model predicted visible mold formation (mold index VTT 3.2) in an internally insulated masonry wall. The findings underline the limitations of generalized standards and emphasize the necessity of region-specific, data-driven indoor climate models for reliable building performance and durability assessment in cold climates such as Latvia.</p>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/4454464","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathogenic Molds and Multidrug-Resistant Respiratory Bacterial Pathogens Isolated From Air Samples Collected From a Clinic in Johannesburg, South Africa","authors":"Lee Heine,&nbsp;Robyn Theresa Dodds,&nbsp;Atheesha Singh","doi":"10.1155/ina/1612232","DOIUrl":"https://doi.org/10.1155/ina/1612232","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Poor air quality contributes to the global burden of respiratory disease, with both bacteria and fungi contributing to the number of deaths associated with community- and healthcare-associated infections, especially those caused by antimicrobial-resistant microbes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>During February and March of 2025, air samples were collected within a Johannesburg clinic directly onto Brilliance <i>Escherichia coli</i>/Coliform Selective, MacConkey Agar, and Potato Dextrose Agar (Oxoid, United Kingdom), which were incubated at 37°C and 32°C, respectively. Resulting bacterial isolates were identified and subjected to antimicrobial susceptibility testing using the Vitek 2 compact system (BioMérieux Inc., France), and unique fungal isolates were identified using polymerase chain reaction amplification of internal transcribed spacer regions and subsequent sequencing.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>After identification, all but three fungal isolates were categorized as pathogens or opportunistic pathogens. All Gram-negative (<i>n</i> = 77; 100%) and almost all Gram-positive (<i>n</i> = 52/54; 96.30%) isolates were categorized as multidrug-resistant. Significant proportions of both Gram-negative and Gram-positive isolates, including members of the ESKAPEE group of pathogens (<i>Enterococcus faecium</i>, <i>Staphylococcus aureus</i>, <i>Klebsiella pneumoniae</i>, <i>Acinetobacter baumannii</i>, <i>Pseudomonas aeruginosa</i>, <i>Enterobacter</i> species, and <i>E. coli</i>), were found to be resistant to antimicrobials used as first-line treatments for respiratory and other infections and antimicrobials of last resort such as colistin and vancomycin.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Almost all microbes identified and characterized in the present study pose a significant risk of infection and a source of spread of antimicrobial resistance. It is, therefore, recommended that the infection prevention and control program at the clinic in question be reviewed and that monitoring continue.</p>\u0000 </section>\u0000 </div>","PeriodicalId":13529,"journal":{"name":"Indoor air","volume":"2026 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ina/1612232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}