Search Results (209)

Over recent decades, phase-change materials (PCMs) have gained prominence as latent-heat thermal energy storage systems in building envelopes because of their high energy density. However, only PCMs that complete a full daily charge–discharge cycle can deliver meaningful energy and carbon-emission savings. This simulation study introduces a methodology that simultaneously optimizes PCM integration for storage efficiency, indoor thermal comfort, and energy savings. Two new indicators are proposed: overall storage efficiency (EC_n), which consolidates heating and cooling-efficiency ratios into a single value, and the performance factor (PF), which quantifies the PCM’s effectiveness in maintaining thermal comfort. Using EnergyPlus v8.9 coupled with DesignBuilder, a residential ASHRAE 90.1 mid-rise apartment was modeled in six warm-temperate (Cfb) European cities for the summer period from June 1 to August 31. Four paraffin PCMs (RT-22/25/28/31 HC, 20 mm thickness) were tested under natural and controlled ventilation strategies, with windows opening 50% when outdoor air was at least 2 °C cooler than indoors. Simulation outputs were validated against experimental cubicle data, yielding a mean absolute indoor temperature error ≤ 4.5%, well within the ±5% tolerance commonly accepted for building thermal simulations. The optimum configuration—RT-25 HC with temperature-controlled ventilation—achieved PF = 1.0 (100% comfort compliance) in all six cities and delivered summer cooling-energy savings of up to 3376 kWh in Paris, the highest among the locations studied. Carbon-emission reductions reached 2254 kg CO₂-e year⁻¹, and static payback periods remained below the assumed 50-year building life at a per kg PCM cost of USD 1. The EC_n–PF framework, therefore, provides a transparent basis for selecting cost-effective, energy-efficient, and low-carbon PCM solutions in warm-temperate buildings. Full article

This study investigates the impact of teacher decisions and other contextual factors on indoor air quality (IAQ) in mechanically ventilated elementary school classrooms using low-cost air quality sensors. Four classrooms at a K–8 school in San Jose, California, were monitored for airborne particulate matter (PM), carbon dioxide (CO₂), temperature, and humidity over seven weeks. Each classroom was equipped with an HVAC system and a portable air cleaner (PAC), with teachers having full autonomy over PAC usage and ventilation practices. Results revealed that teacher behaviors, such as the frequency of door/window opening and PAC operation, significantly influenced both PM and CO₂ levels. Classrooms with more active ventilation had lower CO₂ but occasionally higher PM_2.5 due to outdoor air exchange, while classrooms with minimal ventilation showed the opposite pattern. An analysis of PAC filter material and PM morphology indicated distinct differences between indoor and outdoor particle sources, with indoor air showing higher fiber content from clothing and carpets. This study highlights the critical role of teacher behavior in shaping IAQ, even in mechanically ventilated environments, and underscores the potential of low-cost sensors to support informed decision-making for healthier classroom environments. Full article

The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO₂ and PM_2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city’s busiest transport corridors, analyzing how the concentrations of CO₂, PM_2.5, and PM₁₀, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty’s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems. Full article

We surveyed the air quality conditions in 18 homes with gas stoves for PM_2.5, CO₂, NO₂ and CO using calibrated low-cost sensors. In each home, participants were asked to cook as usual, but to record their cooking activities and mitigation efforts (windows, ventilation fans, etc.). All homes showed enhanced pollutants during, and immediately after, times of cooking or stove use. For each home, we quantified the minutes per day and minutes per minute of cooking over known health thresholds for each pollutant. On average, homes exhibited 38 min per day over one or more of these thresholds, with PM_2.5 and NO₂ being the pollutants of greatest concern. Six homes had much higher occurrences over the health thresholds, averaging 73 min per day. We found an average of 1.0 min over one or more of the health thresholds per minute of cooking when no mitigation was used, whereas when mitigation was used (filtration or vent fan), this value was reduced by 34%. We further investigated several mitigation methods including natural diffusion, a commercial HEPA filter unit, a commercial O₃ scrubber and a ventilation fan. We found that the HEPA unit was highly effective for PM_2.5 but had no impact on any of the gaseous pollutants. The O₃ scrubber was moderately effective for NO₂ but had little impact on the other pollutants. The ventilation fan was highly effective for all pollutants and reduced the average pollutant lifetime significantly. Under controlled test conditions, the pollutant lifetime (or time to reach 37% of the original concentration), was reduced from an average of 45 min (with no ventilation) to 7 min. While no commercial filter showed efficacy for both PM_2.5 and NO₂, the fact that each could be removed individually suggests that a combined filter for both pollutants could be developed, which would significantly reduce health impacts in homes with gas stoves. Full article

The advancement of high-tech industries, notably in semiconductor manufacturing, pharmaceuticals, and precision instrumentation, has imposed stringent requirements on cleanroom environments, where strict control of airborne particulates, microbial presence, temperature, and humidity is essential. However, these controlled environments incur significant energy consumption, with air conditioning systems accounting for 40–60% of total usage due to high air circulation rates, intensive treatment demands, and system resistance. In light of global carbon reduction goals and escalating energy costs, improving the energy efficiency of cleanroom heating, ventilation, and air conditioning (HVAC) systems has become a critical research priority. Recent efforts have focused on optimizing airflow distribution, integrating heat recovery technologies, and adopting low-resistance filtration to reduce energy demand while maintaining stringent environmental standards. Concurrently, artificial intelligence (AI) methods, such as machine learning, deep learning, and adaptive control, are being employed to enable intelligent, energy-efficient system operations. This review systematically examines current energy-saving technologies and strategies in cleanroom HVAC systems, assesses their real-world performance, and highlights emerging trends. The objective is to provide a scientific basis for the green design, operation, and retrofit of cleanrooms, thereby supporting the industry’s transition toward low-carbon, sustainable development. Full article

Heating, ventilation, and air-conditioning systems account for 40–60% of the energy consumed in commercial buildings, and much of this load originates from sub-optimal piping layouts in chiller-plant rooms. This study presents an automated routing framework that couples Building Information Modeling (BIM) with an enhanced A* search to produce collision-free, low-resistance pipelines while simultaneously guiding component selection. The algorithm embeds protective buffer zones around equipment, reserves maintenance corridors through an attention-based cost term, and prioritizes 135° elbows to cut local losses. Generated paths are exported as Industry Foundation Classes (IFC) objects for validation in a BIM digital twin, where hydraulic feedback drives iterative reselection of high-efficiency devices—including magnetic-bearing chillers, cartridge filters and tilted-disc valves—until global pressure drop and life-cycle cost are minimized. In a full-scale shopping-mall retrofit, the method significantly reduces pipeline resistance and operating costs, confirming its effectiveness and replicability for sustainable chiller-plant design. Full article

Neonatal respiratory distress is a primary contributor to neonatal morbidity and mortality worldwide. Non-invasive respiratory support such as nasal continuous positive airway pressure (NCPAP) and bubble NCPAP (bNCPAP) are often used as the first line of treatment for neonatal respiratory distress, including respiratory distress syndrome; however, many hospitals in low- and middle-income countries do not have access to advanced respiratory support devices beyond NCPAP. A novel, non-invasive bubble positive pressure ventilation device has been developed as a low-cost, non-electric alternative to providing respiratory support in such scenarios. In this article, we propose evidence-based guidelines for the initiation, titration, and weaning of the new device. Full article

This Technical Note presents the design and implementation of a low-cost modular growth chamber developed to keep mother plants under controlled environmental conditions for vegetative propagation. The system was conceived as an accessible alternative to expensive commercial equipment, offering reproducibility and adaptability for small-scale and research-based cultivation. The proposed chamber integrates thermal insulation, LED lighting, forced ventilation through the implementation of extractors, a recirculating irrigation system with double filtration, and a sensor-based environmental monitoring platform operated via an Arduino UNO microcontroller. The design features a removable tray that serves as a support for the mother plant, an observation window covered by a movable dark acrylic that prevents the passage of external light, and a vertical structure that facilitates optimal space utilization and ergonomic access. Functionality was conducted using a Stevia rebaudiana Bertoni mother plant maintained for 30 days under monitored conditions. Environmental parameters—temperature, relative humidity, and illuminance—were recorded continuously. The plant showed vegetative development through new shoot emergence and the growth in height of the plant, and despite a loss in foliage expansion, it confirmed the chamber’s capacity to support sustained growth. Although no statistical replication or control group was included in this preliminary evaluation, the system demonstrates technical feasibility and practical utility. This chamber provides a replicable platform for future experimentation and propagation studies. Complete technical specifications, schematics, and component lists are provided to enable replication and further development by other researchers. The growth chamber design aligns with the goals of open-source agricultural innovation and supports knowledge transfer in controlled-environment plant propagation technologies. Full article

This study proposes sustainable natural ventilation strategies using the periodic opening and closing of windows and doors to maintain acceptable indoor air quality in a small office space during the winter season. Field experiments were conducted in a 26.8 m² university office room in Seoul, Korea, measuring the indoor and outdoor temperature, humidity, wind speed, carbon dioxide concentration, and fine dust levels. A simulation model based on a first-order differential equation was developed using EES software (version 9) to predict indoor CO₂ concentrations at one-minute intervals. The simulation results showed good agreement with the experimental data, validating the accuracy of the modeling approach. Based on the validated model, practical ventilation durations and intervals were derived according to the occupant number and room volume, ensuring that indoor CO₂ concentrations remained below the recommended 1000 ppm threshold. The results demonstrate that simple, periodic natural ventilation is effective in maintaining acceptable indoor air quality. As a passive strategy requiring no electrical energy, it offers a sustainable and low-cost solution for creating a healthy indoor environment. Full article

Particulate matter (PM) is recognized as a leading health risk factor worldwide, causing adverse effects for people in living and working environments. During the COVID-19 pandemic, it was shown that ultrafine particles (UFP) and PM concentrations, may have played an important role in the transmission of SARS-CoV-2. This study aims to investigate whether the mechanical ventilation system installed as a COVID-19 mitigation measure in a university dining hall can be effectively and sustainably used to improve indoor UFP exposure levels, integrated with a continuous low-cost sensor monitoring system. Measurements of particle number concentration (PNC), average diameter (D_avg), and Lung Deposited Surface Area (LDSA) were performed over three working days divided into ten homogeneous daily time slots (from 12:00 am to 11:59 pm) using high-frequency (1 Hz) real-time devices. PM and other indoor pollutants (CO₂ and TVOC) were monitored using low-cost handheld sensors. Indoor PNC (Dp < 700 nm) increased and showed great variability related to dining activities, reaching a maximum average PNC level of 30,000 part/cm³ (st. dev. 16,900). D_avg (Dp < 300 nm) increased during lunch and dinner times, from 22 nm at night to 48 nm during post-dinner recovery activities. Plasma-based filter technology reduced average PNC (Dp < 700 nm) by up to three times, effectively mitigating UFP concentrations in indoor environments, especially during dining hall access periods. It could be successfully adopted also after the pandemic emergency, as a sustainable health and safety control measure to improve UFPs exposure levels. Full article

Seasonal, diurnal, and site-to-site variations in indoor PM_2.5 concentrations in Akure, a city in southwestern Nigeria, are investigated by continuous observations using low-cost sensors in six households. Significant seasonal variations were observed, with the highest monthly PM_2.5 concentrations occurring in the dry season, both indoors and outdoors. Significant seasonal variations with higher PM_2.5 levels during the dry season were observed, with mean PM_2.5 concentrations of 55 μg/m³ in the kitchen and 48 μg/m³ in the living rooms, compared to those during the wet season (23 μg/m³ in the kitchen and 14 μg/m³ in the living rooms). The kitchen-to-outdoor and indoor-to-outdoor PM_2.5 ratios increased particularly during the morning and evening hours at several sites, suggesting significant contributions from cooking activities in the kitchen, as well as the transfer of PM_2.5 into the living room. An assessment of PM_2.5 exposure risks among 32 residents in the studied households revealed higher risks among individuals who cook routinely. This study underscores the importance of addressing indoor air pollution alongside outdoor pollution, particularly by improving ventilation and reducing cooking emissions, to effectively minimize exposure risks. Full article

Ventilator-associated pneumonia (VAP) remains a significant concern in intensive care units (ICUs), contributing to increased morbidity, mortality, and healthcare costs. Probiotics and synbiotics have been explored as potential preventive measures due to their ability to modulate gut microbiota, reduce pathogenic colonization, enhance immune responses, and maintain intestinal barrier integrity. While some randomized controlled trials (RCTs) suggest that specific strains, such as Lactobacillus rhamnosus GG and Bifidobacterium breve, may reduce VAP incidence, larger trials have not confirmed significant benefits. Systematic reviews and meta-analyses indicate a potential 28–38% relative risk reduction in VAP, but evidence quality remains low due to methodological limitations and study heterogeneity. Economic evaluations also question the cost effectiveness of probiotic use in ICU settings. Future research should focus on large-scale, multicenter RCTs to determine the optimal strains, dosages, and administration methods, along with standardized diagnostic criteria. Until stronger evidence emerges, probiotics should be considered an adjunctive rather than a primary VAP prevention strategy. Full article

This study presents a low-cost and scalable CO₂ monitoring system that leverages NDIR sensors and a Long Short-Term Memory (LSTM) neural network to predict indoor CO₂ concentrations over both short- and long-term horizons. The proposed system aims to anticipate air quality deterioration in shared spaces, enabling proactive ventilation strategies. Various LSTM configurations were evaluated, optimizing the number of layers, neurons per layer, and input delays to enhance forecasting accuracy. The optimal model consisted of two LSTM layers with 128 neurons each and a time window of 10 previous observations. This model achieved an RMSE of approximately 57 ppm for an 8 h forecast in a classroom setting. Experimental results demonstrate the reliability of the proposed approach for CO₂ prediction and its potential impact on indoor air quality management. Full article

Background: The indoor environment can contribute to dry eye disease (DED) risk, but the effects of environmental modifications on disease are still uncertain. This study evaluated the effect of home interventions that modify the indoor environment on DED symptoms and sign severity. Methods: The prospective study consisted of two visits (6 ± 1 months apart). At each home visit, indoor environmental conditions (temperature, humidity, and airborne particulate matter) were monitored and at each clinical visit, DED symptoms and signs were examined. After the first visit, all participants received a report of their home air quality and 10 recommendations to improve their home environment. At the 6-month visit, participants indicated which interventions they implemented. Results: A total of 99 subjects participated in the clinical evaluation and home monitoring at baseline and six-month follow-up. Their mean age was 61 years, and 26% identified as Hispanic. Most had mild or greater DED symptoms (5-Item Dry Eye Questionnaire, DEQ5 ≥ 6), with an average DEQ5 score of 10.49 ± 5.51 at baseline. In total, 77% (n = 76) implemented ≥1 intervention with home ventilation (42.4%), air conditioner filter change (36.4%), and exhaust fan use (31.3%) being the most frequent. Overall, with every intervention implemented, tear osmolarity (change from baseline to 6 months) declined by 2% (log-transformed β = 0.02; 95% confidence interval (CI) = 0.00–0.03; p < 0.05), and Meibomian gland (MG) plugging declined by 14% (log-transformed β = 0.14; CI = 0.05–24; p < 0.05). Specific interventions had specific impacts on DED signs and symptoms. For example, osmolarity declined by a greater degree in those that implemented home ventilation, while DED symptoms improved to a greater degree in those that utilized indoor plants compared to those that did not implement these interventions. Conclusions: When provided with an objective report of home environmental conditions and remediation strategies, most participants voluntarily implemented low-cost home interventions, which reduced the severity of select DED symptoms and signs. Full article

The relevance is introducing fourth generation district heating (4GDH), which decreases operation and maintenance costs by utilizing the efficiency of low temperature district heating (LTDH). The aim is to develop a methodology allowing for a more flexible heat demand model and accurate function describing the relationship between outdoor temperature and heat demand. It is represented by a black-box model based on historical data collected from heating, ventilation, and air conditioning (HVAC) systems. Energy delivery/consumption is analyzed with the help of a set of statistical and regression formulas. The analysis of operational data is then transformed to methodology to regulate heat supply with combined heat-and-power (CHP) generation. The key features are that the model takes into account thermal capacity and type of substation; the district heating (DH) plant is not assumed to have a fixed return temperature and generation profile. The novelty is an emphasis on DH operation and introduction of statistics into a dynamic simulation model. With no abnormal buildings, higher accuracy of modeling is achieved. Most of the consumers are pretty similar in thermal response, even though specific energy demand and heated volume may differ. Heat demand of an old building is better simulated with discrete regression, while those with pump-equipped substations are modeled with linear regression. Full article