Search Results (709)

This study investigates the seasonal effectiveness of high-efficiency particulate air (HEPA) purifiers and window-opening behaviors in three London nurseries, using continuous indoor and outdoor PM_2.5 monitoring, window state and air purifier use, and occupant questionnaire data collected from March 2021 to February 2022. Of the approximately 40–50 nurseries contacted, only three agreed to participate. Results show that HEPA purifiers substantially reduced indoor particulate matter (PM_2.5), with the greatest effect observed during the heating season when windows remained closed for longer periods. Seasonal and behavioral analysis indicated more frequent and longer window opening in the non-heating season (windows were open 41.5% of the time on average, compared to 34.2% during the heating season) driven by both ventilation needs and heightened COVID-19 concerns. Predictive modeling identified indoor temperature as the main driver of window opening, while carbon dioxide (CO₂) had a limited effect. In addition, window opening often increased indoor PM_2.5 under prevailing outdoor air quality conditions, with mean concentrations rising from 2.73 µg/m³ (closed) to 3.45 µg/m³ (open), thus reducing the apparent benefit of air purifiers. These findings underscore the complex interplay between mechanical purification and occupant-controlled ventilation, highlighting the need to adapt indoor air quality (IAQ) strategies to both seasonal and behavioral factors in educational settings. Full article

Background/Objectives: Global warming and rising CO₂ concentrations pose significant challenges to plant systems. Amid these pressures, this study contributes to understanding how tropical species respond by simultaneously evaluating reproductive and genetic traits. It specifically investigates the effects of maternal exposure to warming and elevated CO₂ on progeny physiology, genetic diversity, and population structure in Stylosanthes capitata, a resilient forage legume native to Brazil. Methods: Maternal plants were cultivated under controlled treatments, including ambient conditions (control), elevated CO₂ at 600 ppm (eCO₂), elevated temperature at +2 °C (eTE), and their combined exposure (eTEeCO₂), within a Trop-T-FACE field facility (Temperature Free-Air Controlled Enhancement and Free-Air Carbon Dioxide Enrichment). Seed traits (seeds per inflorescence, hundred-seed mass, abortion, non-viable seeds, coat color, germination at 32, 40, 71 weeks) and abnormal seedling rates were quantified. Genetic diversity metrics included the average (A) and effective (Ae) number of alleles, observed (Ho) and expected (He) heterozygosity, and inbreeding coefficient (Fis). Population structure was assessed using Principal Coordinates Analysis (PCoA), Analysis of Molecular Variance (AMOVA), number of migrants per generation (Nm), and genetic differentiation index (Fst). Two- and three-way Analysis of Variance (ANOVA) were used to evaluate factor effects. Results: Compared to control conditions, warming increased seeds per inflorescence (+46%), reduced abortion (−42.9%), non-viable seeds (−57%), and altered coat color. The germination speed index (GSI +23.5%) and germination rate (Gr +11%) improved with warming; combined treatments decreased germination time (GT −9.6%). Storage preserved germination traits, with warming enhancing performance over time and reducing abnormal seedlings (−54.5%). Conversely, elevated CO₂ shortened GSI in late stages, impairing germination efficiency. Warming reduced Ae (−35%), He (−20%), and raised Fis (maternal 0.50, progeny 0.58), consistent with the species’ mixed mating system; A and Ho were unaffected. Allele frequency shifts suggested selective pressure under eTE. Warming induced slight structure in PCoA, and AMOVA detected 1% (maternal) and 9% (progeny) variation. Fst = 0.06 and Nm = 3.8 imply environmental influence without isolation. Conclusions: Warming significantly shapes seed quality, reproductive success, and genetic diversity in S. capitata. Improved reproduction and germination suggest adaptive advantages, but higher inbreeding and reduced diversity may constrain long-term resilience. The findings underscore the need for genetic monitoring and broader genetic bases in cultivars confronting environmental stressors. Full article

Geological storage is an integral element of the green energy transition. Geological formations, such as aquifers, depleted reservoirs, and hard rock caverns, are used mainly for the storage of hydrocarbons, carbon dioxide and increasingly hydrogen. However, potential adverse effects such as ground movements, leakage, seismic activity, and environmental pollution are observed. Existing research focuses on monitoring subsurface elements of the storage, while on the surface it is limited to ground movement observations. The review was carried out based on 191 research contributions related to geological storage. It emphasizes the importance of monitoring underground gas storage (UGS) sites and their surroundings to ensure sustainable and safe operation. It details surface monitoring methods, distinguishing geodetic surveys and remote sensing techniques. Remote sensing, including active methods such as InSAR and LiDAR, and passive methods of multispectral and hyperspectral imaging, provide valuable spatiotemporal information on UGS sites on a large scale. The review covers modelling and prediction methods used to analyze the environmental impacts of UGS, with data-driven models employing geostatistical tools and machine learning algorithms. The limited number of contributions treating geological storage sites holistically opens perspectives for the development of complex approaches capable of monitoring and modelling its environmental impacts. 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

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

The transportation sector significantly contributes to greenhouse gas (GHG) emissions and remains a key research focus on emission quantification and mitigation. Although numerous models exist for estimating vehicle-based emissions, most lack accuracy at regional scales, particularly in remote or underdeveloped areas, including backcountry national parks and mountainous regions lacking basic infrastructure. This study addresses that gap by developing and applying a terrain-adjusted, segment-based methodology to estimate GHG emissions from tourist vehicles in Altai Tavan Bogd National Park, one of Mongolia’s most remote protected areas. The proposed method uses Tier 1 IPCC emission factors but incorporates field-segmented route analysis, vehicle categorization, and terrain-based fuel adjustments to achieve a spatially disaggregated Tier 1 approach. Results show that carbon dioxide (CO₂) emissions increased from 118.7 tons in 2018 to 2239 tons in 2024. Tourist vehicle entries increased from 712 in 2018 to 13,192 in 2024, with 99.1% of entries occurring between May and October. Over the same period, cumulative methane (CH₄) and nitrous oxide (N₂O) emissions were estimated at 300.9 kg and 45.75 kg, respectively. This modular approach is especially suitable for high-altitude, infrastructure-limited regions where real-time emissions monitoring is not feasible. By integrating localized travel patterns with global frameworks such as the IPCC 2006 Guidelines, this model enables more precise and context-sensitive GHG estimates from vehicles in national parks and similar environments. Full article

Liquefied natural gas (LNG) is increasingly used as a marine fuel due to its capacity to significantly reduce emissions of particulate matter, sulfur oxides (SO_x), and nitrogen oxides (NO_x), compared to conventional fuels. In addition, LNG combustion produces less carbon dioxide (CO₂) than conventional marine fuels, and the use of non-fossil LNG offers further potential for reducing greenhouse gas emissions. However, this benefit can be partially offset by methane slip—the release of unburned methane in engine exhaust—which has a much higher global warming potential than CO₂. This study presents an experimental evaluation of methane emissions from a RoPax vessel powered by low-pressure dual-fuel four-stroke engines with a direct mechanical propulsion system. Methane slip was measured directly during onboard testing and combined with a year-long analysis of engine operation using an Engine Load Monitoring (ELM) method. The yearly average methane slip coefficient (C_slip) obtained was 1.57%, slightly lower than values reported in previous studies on cruise ships (1.7%), and significantly lower than the default values specified by the FuelEU (3.1%) Maritime regulation and IMO (3.5%) LCA guidelines. This result reflects the ship’s operational profile, characterized by long crossings at high and stable engine loads. This study provides results that could support more representative emission assessments and can contribute to ongoing regulatory discussions. Full article

In this study, we comprehensively analyzed material consumption (fuel, hydraulic oil, lubricants, and AdBlue fluid) and estimated carbon dioxide emissions during logging operations. This study was carried out in the northeastern part of Poland. Four harvesters and four forwarders representing two manufacturers (John Deere-Deere & Co., Moline, USA, and Komatsu Forest AB, Umeå, Sweden) were analyzed to compare their operational efficiency and constructional influences on overall operating costs. Due to differences in engine emission standards, approximate greenhouse gas emissions were estimated. The results indicate that harvesters equipped with Stage V engines have lower fuel consumption, while large forwarders use more consumables than small ones per hour and cubic meter of harvested and extracted timber. A strong positive correlation was observed between total machine time and fuel consumption (r = 0.81), as well as between machine time and total volume of timber harvested (r = 0.72). Older and larger machines showed about 40% higher combustion per unit of wood processed. Newer machines meeting higher emission standards (Stage V) generally achieved lower CO₂ and other GHG emissions compared to older models. Machines with Stage V engines emitted about 2.07 kg CO₂ per processing of 1 m³ of wood, while machines with older engine types emitted as much as 4.35 kg CO₂ per 1 m³—roughly half as much. These differences are even more pronounced in the context of nitrogen oxide (NO_x) emissions: the estimated NO_x emissions for the older engine types were as high as ~85 g per m³, while those for Stage V engines were only about 5 g per m³ of harvested wood. Continuing the study would need to expand the number of machines analyzed, as well as acquire more detailed performance data on individual operators. A tool that could make this possible would be fleet monitoring services offered by the manufacturers of the surveyed harvesters and forwards, such as Smart Forestry or Timber Manager. Full article

Validating satellite data is essential to ensure its accuracy, reliability, and practical applicability. Such validation underpins scientific research, operational use, and informed policymaking by confirming that space-based measurements reflect real-world conditions. This is typically achieved by comparing satellite observations with ground-based measurements or established reference standards. Without thorough validation, data integrity is compromised, which can negatively affect decisions and economic outcomes. In this study, we validated data from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) by comparing it with ground-based measurements from the South African Air Quality Information System (SAAQIS). The analysis focused on three monitoring stations—Kliprivier, Lephalale, and Middelburg—over the course of 2022. The pollutants examined include sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO). The results indicate that CO was the predominant pollutant across all sites, particularly in industrial areas. The study also found that satellite data generally overestimated pollution levels, especially during the winter months, emphasizing the importance of robust ground-based validation. Additionally, data quality challenges such as gaps and temporal misalignments affected the accuracy of both satellite and ground datasets. Lastly, the study shows the discrepancy between the ground-based instruments in South Africa and the TROPOMI, and it suggests how these instruments can be incorporated to provide a better understanding of the air quality. Full article

Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article

Mars’ south polar cap hosts dynamic landforms known as Swiss cheese features (SCFs), which form through the sublimation of carbon dioxide (CO₂) ice driven by the planet’s extreme seasonal and diurnal solar insolation cycles. These shallow, rounded depressions—first identified by Mars Global Surveyor in 1999 and later monitored by the Mars Reconnaissance Orbiter (MRO)—have been observed to increase in size over time. However, large-scale analysis of SCF formation and growth has been limited by the slow and labor-intensive nature of manual mapping techniques. This study applies object-based image analysis (OBIA) to automate the detection and measurement of SCFs using High-Resolution Imaging Science Experiment (HiRISE) images spanning five Martian years. Results show that SCFs exhibit a near-linear increase in area, suggesting that summer sublimation consistently outpaces winter CO₂ deposition. Validation against manual digitization shows discrepancies of less than 1%, confirming the reliability of the OBIA method. Regression-based extrapolation of growth trends indicates that the current generation of SCFs likely began forming between Martian years 7 and 16, approximately corresponding to Earth years 1976 to 1998. These findings provide a quantitative assessment of SCF evolution and contribute to our understanding of recent climate-driven surface changes on Mars. HiRISE images were processed using the eCognition software to detect, classify, and measure SCFs, demonstrating that OBIA is a scalable and effective tool for analyzing dynamic planetary landforms. Full article

Acid gas injection (AGI), which primarily involves injecting hydrogen sulfide (H₂S) and carbon dioxide (CO₂), is recognized as a cost-efficient and environmentally sustainable method for controlling sour gas emissions in oil and gas operations. This review examines case studies of twelve AGI projects conducted in Canada, Oman, and Kazakhstan, focusing on reservoir selection, leakage potential assessment, and geological suitability evaluation. Globally, several million tonnes of acid gases have already been sequestered, with Canada being a key contributor. The study provides a critical analysis of geochemical modeling data, monitoring activities, and injection performance to assess long-term gas containment potential. It also explores AGI’s role in Enhanced Oil Recovery (EOR), noting that oil production can increase by up to 20% in carbonate rock formations. By integrating technical and regulatory insights, this review offers valuable guidance for implementing AGI in geologically similar regions worldwide. The findings presented here support global efforts to reduce CO₂ emissions, and provide practical direction for scaling-up acid gas storage in deep subsurface environments. Full article

Data centers contribute to roughly 1% of global energy consumption and 0.3% of worldwide carbon dioxide emissions. The cooling system alone constitutes a substantial 50% of total energy consumption for data centers. Lowering Power Usage Effectiveness (PUE) of data center cooling systems from 2.2 to 1.4, or even below, is one of the critical issues in this thermal management area. In this work, a digital twin system of an Intelligent Data Center (IDC) prototype is designed to be capable of real-time monitoring the temperature distribution. Moreover, aiming to lower PUE, Deep Q-Learning Network (DQN) is further established to make optimization decisions of thermal management during cooling down of the local hotspot. The entire process of thermal management for IDC can be real-time visualized in Unity, forming the virtual entity of data center prototype, which provides an intelligent solution for sustainable data center operation. Full article

Despite spending a substantial proportion of their time indoors, the mental health effects of indoor air pollution on children and adolescents remain inadequately explored. This study aimed to elucidate the spatiotemporal variations and sociodemographic inequalities in exposure to multiple indoor pollutants and to assess their potential associations with depressive symptoms among school-aged children in Beijing. Using real-time portable monitors, concentrations of fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), carbon dioxide (CO₂), formaldehyde (HCHO), total volatile organic compounds (TVOC), temperature, and humidity in classrooms and bedrooms were measured during both weekdays and weekends. Moreover, substantial spatiotemporal heterogeneity was observed. It was found that concentrations of PM_2.5, PM₁₀, and TVOC peaked in classrooms during weekday daytime, while CO₂ levels were highest in bedrooms on weekend nights. Exposure levels were notably higher among children whose mothers had lower educational attainment and those living in recently renovated homes, indicating marked socio-demographic disparities. In multivariable logistic regression models, indoor exposure to CO₂ and TVOC was significantly associated with increased odds of depressive symptoms. These findings highlight the critical need to improve indoor air quality through enhanced ventilation and the mitigation of emissions from indoor sources, particularly within school and residential settings. The results offer valuable empirical evidence to guide the development of targeted environmental interventions and public health policies designed to support and enhance the psychological well-being of children. Full article

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO₂), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO₂ measurement is activated when the specified T and $RH$ change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and $RH$ signals, the number of CO₂ measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO₂ measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO₂ concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Full article