Search Results (136)

The vertical distribution of volatile organic compounds (VOCs) within the planetary boundary layer (PBL) is critical for understanding ozone (O₃) formation, yet knowledge remains limited in complex urban environments. In this study, vertical measurements of 117 VOC species were conducted using an unmanned aerial vehicle (UAV) equipped with a VOC multi-channel sampling system, up to a height of 500 m in Shenzhen, China. Results showed that total VOC (TVOC) concentrations decreased with altitude in the morning, reflecting the influence of surface-level local emissions, but increased with height at midday, likely driven by regional transport and potentially stronger photochemical processes. Source apportionment revealed substantial industrial emissions across all altitudes, vehicular emissions concentrated near the surface, and biomass burning primarily impacting higher layers. Clear evidence of enhanced secondary formation of oxygenated VOCs (OVOCs) was observed along the vertical gradient, particularly at midday, indicating intensified photochemical processes at higher altitudes. These findings underscore the importance of considering vertical heterogeneity in VOC distributions when modeling O₃ formation or developing measures to reduce emissions at different altitudes, and also demonstrate the potential of UAV platforms to provide high-resolution atmospheric chemical data in complex urban environments. Full article

Human cognitive performance is influenced by external factors, including indoor environmental quality (IEQ). Understanding how these factors affect stress, attention, and relaxation is essential in environments such as workplaces and educational institutions, where cognitive function directly impacts performance. This study examines the effects of IEQ on students’ attention and relaxation levels during various lecture periods, focusing on design major students. Three key IEQ parameters (air temperature, relative humidity, and natural lighting) were evaluated for their effects on cognitive states using electroencephalogram (EEG) measurements in a controlled setting. Participants wore non-invasive, portable EEG devices to monitor neurophysiological activity across two sessions, each involving four scenarios: (i) baseline, (ii) increased natural light exposure, (iii) elevated relative humidity, and (iv) increased air temperature. EEG-derived metrics of attention and relaxation were analyzed alongside environmental data, including temperature, humidity, lighting conditions, carbon dioxide (CO₂) concentration, total volatile organic compounds (TVOC), and particulate matter (PM), to identify potential correlations. Results showed that natural light exposure improved relaxation but reduced attention, suggesting a restorative effect on stress that may also introduce distractions. Attention peaked under moderately warm, dry conditions (25–26 °C and 16–19% relative humidity), correlating positively with temperature (Pearson correlation coefficient, $r$ = 0.32) and negatively with humidity ($r$ = −0.50). Conversely, relaxation was highest under cooler, more humid conditions (23–24 °C and 24–26% relative humidity). Attention was negatively correlated with CO₂ ($r$ = −0.47) and PM_2.5 ($r$ = −0.46), suggesting that poor air quality impairs alertness. Relaxation showed weaker but positive correlations with PM_2.5 ($r$ = 0.38), PM_1.0 ($r$ = 0.35), and CO₂ ($r$ = 0.32). Ultrafine particles (PM_0.3, PM_0.5) and TVOC had minimal association with cognitive states. Overall, this study underscores the importance of optimizing indoor environments in educational settings to enhance academic performance and supports the development of evidence-based design standards to foster healthy, effective learning environments. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. 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

The acceleration of industrialization and urbanization worldwide has dramatically improved living standards but has also introduced serious environmental and public health challenges. One of the most critical challenges is air pollution, particularly indoors, where individuals typically spend over 90% of their time. Ensuring good Indoor Air Quality (IAQ) is essential, especially in heavily frequented public spaces such as shopping malls. This study focuses on assessing IAQ in a large shopping mall located in Tabuk, Saudi Arabia, covering retail zones as well as an attached underground parking area. Monitoring is conducted over a continuous two-month period using calibrated instruments placed at representative locations to capture variations in pollutant levels. The investigation targets key contaminants, including carbon monoxide (CO), carbon dioxide (CO₂), fine particulate matter (PM_2.5), total volatile organic compounds (TVOCs), and formaldehyde (HCHO). The data are analyzed and compared against international and national guidelines, including World Health Organization (WHO) standards and Saudi environmental regulations. The results show that concentrations of CO, CO₂, and PM_2.5 in the shopping mall are generally within acceptable limits, with values ranging from approximately 7 to 15 ppm, suggesting that ventilation systems are effective in most areas. However, the study identifies high levels of TVOCs and HCHO, particularly in zones characterized by poor ventilation and high human occupancy. Peak concentrations reach 1.48 mg/m³ for TVOCs and 1.43 mg/m³ for HCHO, exceeding recommended exposure thresholds. These findings emphasize the urgent need for enhancing ventilation designs, prioritizing the use of low-emission materials, and establishing continuous air quality monitoring protocols within commercial buildings. Improving IAQ is not only crucial for protecting public health but also for enhancing occupant comfort, satisfaction, and overall building sustainability. This study offers practical recommendations to policymakers, building managers, and designers striving to create healthier indoor environments in rapidly expanding urban centers. Full article

This study was conducted in an urban park in a temperate-continental city of Europe (Timișoara, Romania) and aimed to investigate the contribution of urban vegetation in maintaining air quality and mitigating the heat in the analyzed city. The following air parameters were monitored: fine particulate matter PM_2.5, coarse particulate matter PM₁₀, AQI (Air Quality Index) (resulted from PM_2.5 and PM₁₀), particle number, air temperature, relative air humidity, TVOC (total volatile organic compounds), and HCHO (formaldehyde). The results of this study show that urban vegetation remains a reliable factor in reducing PM_2.5 and PM₁₀ in city air and in keeping the AQI within the limits corresponding to good air quality, but also that relative air humidity counteracts the contribution of vegetation in achieving this goal. Inside the park, the HCHO concentration increased by up to 4–5 times compared to the outside, and this increase was not caused by vehicle traffic but rather by the photochemical reactions generating HCHO. Regarding the cooling effect on air temperature, the studied green space did not exhibit this effect, as the air temperature inside it increased by up to 1–6 °C compared to the outside. Our results contrast with the general perception that urban parks and green spaces are cooler islands within the cities and draw attention to the fact that having a green space in a city does not necessarily mean achieving environmental goals, such as reducing the heat risk of cities. Based on the results, we consider that the main limitations in achieving these objectives were the park’s small size (88 hectares) and its morphology and architecture resulting from the integration of the species that compose it. It follows from these data that it is not enough for an urban green space to be established, but its design must be combined with urban morphology strategies if the heat mitigation effect is to be achieved and the cooling benefits are to be maximized in cities. Full article

In this research, we present the development and validation of a compact, resource-efficient (low-cost, low-energy), distributed, real-time traffic and air quality monitoring system. Deployed since November 2023 in a small town that relies on burning various fuels and waste for winter heating, the system comprises three IoT units that integrate image processing and environmental sensing for sustainable urban and environmental management. Each unit uses an embedded camera and sensors to process live data locally, which are then transmitted to a central database. The image processing algorithm counts vehicles by type with over 95% daylight accuracy, while air quality sensors measure pollutants including particulate matter (PM), equivalent carbon dioxide (eCO₂), and total volatile organic compounds (TVOCs). Data analysis revealed fluctuations in pollutant concentrations across monitored areas, correlating with traffic variations and enabling the identification of pollution sources and their relative impacts. Recorded PM10 daily average levels even reached eight times above the safe 24 h limits in winter, when traffic values were low, indicating a strong link to household heating. This work provides a scalable, cost-effective approach to traffic and air quality monitoring, offering actionable insights for urban planning and sustainable development. Full article

Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. To detect early fire risk indicators, the system processes data from 62,630 sensor measurements across 15 ecological parameters, including temperature, humidity, total volatile organic compounds (TVOC), CO₂ levels, and particulate matter. A comparative analysis of six machine learning models—Logistic Regression, Support Vector Machines (SVM), Random Forest, and proposed EvacuNet—demonstrates that EvacuNet outperforms all other models, achieving an accuracy of 99.99%, precision of 1.00, recall of 1.00, and an AUC-ROC score close to 1.00. The predictive alarm system significantly reduces false alarm rates and enhances fire detection speed, allowing emergency responders to take preemptive action. Moreover, integrating AI-driven evacuation optimization minimizes bottlenecks and congestion, reduces evacuation times, and improves structured crowd movement. These findings underscore the necessity of intelligent fire detection systems in high-occupancy venues, demonstrating that AI-based predictive modeling can drastically improve fire response and evacuation efficiency. Future research should focus on integrating IoT-enabled emergency navigation, reinforcement learning algorithms, and real-time crowd management systems to further enhance predictive accuracy and minimize casualties. By adopting such advanced technologies, large-scale venues can significantly improve emergency preparedness, reduce evacuation delays, and enhance public safety. Full article

Eucalyptus wood particleboard (EPB), commonly used in indoor decoration, releases volatile organic compounds (VOCs) that can adversely affect indoor air quality and human health. This study systematically examined the VOC emission characteristics of EPB using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography mass spectrometry (GC-MS). A total of 65 VOCs were identified, with medium-volatility organic compounds (MVOCs) accounting for 28 compounds, low-volatility organic compounds (LVOCs) for 26, and high-volatility organic compounds (HVOCs) for 11. Terpenoids dominated the VOCs, comprising 78.46%, followed by aldehydes (10.77%) and alkanes (7.69%). Key odorant compounds (KOCs) were identified using the relative odor activity value (ROAV), with hexanal (ROAV = 100) and o-cymene (ROAV = 76.90) emerging as the most significant contributors to the overall odor profile. Thermal post-treatment at temperatures of 50–60 °C for durations of 6–12 h was found to be an effective method for reducing the residual VOCs and KOCs in the EPB, leading to a marked decrease in the peak areas of key odorants. The findings suggest several strategies for minimizing VOC emissions and eliminating residual odor, including reducing the use of miscellaneous wood materials, controlling the production of o-cymene, and employing thermal post-treatment at moderate temperatures. These measures provide a promising approach to reducing VOC and odor emissions from EPB and similar composite wood products, thereby enhancing their suitability for indoor applications. This study innovatively establishes an evaluation system for VOC emission characteristics in wood-based panels based on the ROAV. It elucidates the contribution mechanisms of key odor-active substances (e.g., hexanal and pentanal) and presents a thermal post-treatment process for source control, achieving simultaneous VOCs and odor elimination. A ROAV-guided hierarchical management strategy is proposed, providing scientific guidelines for the industrial production of high-quality particleboards with ultralow emissions (TVOC < 50 μg/m³) and minimal odor intensity (OI < Grade 3). Full article

Noise level and atmospheric pollutants such as particulate matter (PM) and combustion gases depend on car traffic. A highly circulated area in Cluj-Napoca City (Romania) was investigated based on sustainable principles. The noise level at the source was about 77 dB due to intense traffic of 214 units/10 min including cars, buses, trucks and pedestrians. It decreased slowly to 62 dB in the proximity of the first wall from the roadside (20 m distance) and significantly to about 50 dB beyond the buildings near the road (135 m from the source). The noise level was correlated with a high pollutant emission at the source: 25 µg/m³ (PM_2.5), 45 µg/m³ (PM₁₀) and 1.023 µg/m³ (total volatile organic compounds (TVOCs)). The PM₁₀ level decreased to 38 µg/m³, while PM_2.5 remained at a high level at 22 µg/m³ and TVOC slightly decreased to 0.974 µg/m³ at the distance of 10 m from the road. The PM_2.5 and PM₁₀ levels decreased significantly to 5 µg/m³ (PM₁₀) and 18 µg/m³ (PM_2.5) at a distance of 135 m from the road, and the TVOC level also decreased significantly to about 0.705 µg/m³. The PM crystalline structure was investigated by XRD coupled with mineralogical microscopy. Microstructure and elemental composition were assessed via high-resolution SEM coupled with EDS spectroscopy. It was found that PM_2.5 was dominated by the finest clay fraction (e.g., kaolinite and muscovite), while PM₁₀ was dominated by quartz and calcite. A large amount of organic matter was found adsorbed onto the finest particles by FTIR spectroscopy. The correlation between PM emissions and sound intensity indicates that intense noise extended for a long time facilitates the ultra-structural PM fraction sustentation into the atmosphere. A large number of clay nanoparticles (kaolinite 40 nm and muscovite 60 nm) were detected by AFM in the samples collected at the noise source, becoming sparser at a distance of 135 m from the source. Full article

The air quality in Mosul was adversely affected both directly and indirectly during and after the conflict phase, spanning from the occupation to the liberation of the city from ISIS (2014–2017). Direct impacts included the ignition of oil fields and sulphur deposits, as well as the use of military weapons and their propellants. Indirectly, the air quality was also compromised by various other factors negatively affecting the quality due to excessive emission levels of air pollutants, such as particulate matter (PM), sulphur dioxide (SO₂), nitrogen dioxide (NO₂) and other toxic gases. Six important locations in the city of Mosul were selected, and the concentrations of the parameters PM2.5, PM10, formaldehyde (HCHO), total volatile organic compounds (TVOC), NO₂ and SO₂ were determined at monthly intervals during the year 2022. The sites were selected both according to their proximity and their specific distance from the direct conflict zone. The aim was to assess the present pollutant levels based on WHO guidelines and to compare the results with previous pre-war studies to understand the long-term war impact on air quality. The results showed that the annual average values of PM2.5, PM10 and NO₂ were above the WHO limits at all locations throughout the year. In contrast, the annual average values of TVOC, HCHO and SO₂ were within the limits in the hot months but exceeded them in the cold months (December to March), which can be attributed to the use of heating material in winter. Two sites revealed higher pollution levels than the others, which can be attributed to their proximity to the devastated areas (conflict zones), high traffic density and a high density of power generators. These factors were further exacerbated by post-war migration from the destroyed and unsafe areas. Thus, in addition to the short-term effects of burning oil fields and sulphur deposits, as well as airborne weapon emissions, the increase in traffic, the use of decentralized power generators, and the higher demand for heating oil, progressive desertification due to deforestation and the destruction of extensive green areas, as well as increasing and unaddressed environmental violations in general, can be held responsible for declining air quality in the urban area. This work should be considered as preliminary work to emphasise the urgent need for conventional air quality monitoring to consolidate air quality data and monitor the effectiveness of different approaches to mitigate war-related air quality deterioration. Possible approaches include the implementation of air purification technologies, the preservation of existing ecosystems, the replacement of fossil energy sources with renewable energy options, proactive and sustainable urban planning and enforcing strict air quality regulations and policies to control and reduce pollution levels. Full article

This study investigates the chemical complexity and toxicity of volatile organic compounds (VOCs) emitted from national petrochemical industrial parks and their effects on air quality in an industrial area of Nanjing, China. Field measurements were conducted from 1 December 2022, to 17 April 2023, focusing on VOC concentrations and speciations, diurnal variations, ozone formation potential (OFP), source identification, and associated health risks. The results revealed an average total VOC (TVOC) concentration of 15.9 ± 12.9 ppb and an average OFP of 90.1 ± 109.5 μg m⁻³. Alkanes constituted the largest fraction of VOCs, accounting for 44.1%, while alkenes emerged as the primary contributors to OFP, comprising 52.8%. TVOC concentrations peaked before dawn, a pattern attributed to early morning industrial activities and nighttime heavy vehicle operations. During periods classified as clean, when ozone levels were below 160 μg m⁻³, both TVOC (15.9 ± 12.9 ppb) and OFP (90.4 ± 110.0 μg m⁻³) concentrations were higher than those during polluted hours. The analysis identified the key sources of VOC emissions, including automobile exhaust, oil and gas evaporation, and industrial discharges, with additional potential pollution sources identified in adjacent regions. Health risk assessments indicated that acrolein exceeded the non-carcinogenic risk threshold at specific times. Moreover, trichloromethane, 1,3-butadiene, 1,2-dichloroethane, and benzene were found to surpass the acceptable lifetime carcinogenic risk level (1 × 10⁻⁶) during certain periods. These findings highlight the urgent need for enhanced monitoring and regulatory measures aimed at mitigating VOC emissions and protecting public health in industrial areas. In the context of complex air pollution in urban industrial areas, policymakers should focus on controlling industrial and vehicle emissions, which can not only reduce secondary pollution, but also inhibit the harm of toxic substances on human health. Full article

In modern society, individuals spend an increasing amount of time indoors, emphasizing the importance of understanding the health impacts of indoor environments. This study focused on measuring indoor air quality to identify vulnerable populations and observe the effects of residential environment improvements on air quality. Targeting low-income families and elderly households, known for their heightened vulnerability to environmental health risks, the study involved direct visits to 2328 low-income households across 16 cities and provinces in South Korea from 2021 to 2022. Indoor air quality parameters, including PM_2.5, PM₁₀, total volatile organic compounds (TVOC), formaldehyde (HCHO), and airborne mold, were measured. Among these households, 300 with critically compromised living conditions received support for wallpaper and paneling replacement. Comparative measurements before and after the renovations revealed that single-person households had higher levels of PM_2.5 and TVOC compared with households with four or more members. Additionally, households with elevated concentrations of airborne mold also exhibited higher levels of PM_2.5 and PM₁₀. Importantly, households that received environmental improvements showed a significant reduction in airborne mold concentration by approximately 50% or more. This study underscores the importance of indoor environmental health and provides valuable evidence supporting policies focused on health promotion and residential welfare improvements for vulnerable populations. The research is distinguished by its comprehensive nature, involving direct measurements from nearly 2000 households nationwide, rather than relying solely on secondary data. Full article

This study addresses an identified literature gap regarding indoor environmental quality in residential buildings, where the primary focus has traditionally been on energy performance rather than comfort optimization. Leveraging the low-cost and easy-to-implement LoRaWAN protocol, this research collects and analyses real-time data on comfort parameters, including temperature, CO₂ levels, humidity, lighting, atmospheric pressure, and total volatile organic compounds (TVOC) across various buildings within the INCUBE EU project. The results highlight the dynamic nature of the parameters and emphasize the importance of continuous monitoring to enhance comfort and energy efficiency in smart residential buildings. The findings advocate for integrating technologies like LoRaWAN to optimize indoor environmental quality, ultimately improving residential comfort and occupant well-being. Full article

Volatile organic compounds (VOCs) are ubiquitous in the atmosphere, posing significant adverse impacts on air quality and human health. However, current research on atmospheric VOCs mainly focuses on specific regions or industries, without comprehensive national-level analysis. In this study, a total of 99 articles on atmospheric VOCs in China published from 2015 to 2024 were screened, and data on their concentrations, source apportionment, and health risks were extracted and summarized. The results revealed that the annual average concentrations of TVOCs and their groups in China generally increased and then decreased between 2011 and 2022, peaking in 2018–2019. A distinct seasonal pattern was observed, with the highest concentrations occurring in winter, followed by autumn, spring, and summer. TVOC emissions were highly concentrated in northern and eastern China, mainly contributed by alkanes and alkenes. Source apportionment of VOCs indicated that vehicle sources (32.9% ± 14.3%), industrial emissions (18.0% ± 12.8%), and other combustion sources (13.0% ± 13.0%) were the primary sources of VOCs in China. There was a significant positive correlation (p < 0.05) between the annual mean VOC concentration and population size, and a notable negative correlation (p < 0.05) with GDP per capita. Atmospheric VOCs had no non-carcinogenic risk (HI = 0.5) but exhibited a probable carcinogenic risk (7.5 × 10⁻⁵), with relatively high values for 1,2-dibromoethane, 1,2-dichloroethane, and naphthalene. The health risk was predominantly driven by halocarbons. These findings are essential for a better understanding of atmospheric VOCs and for developing more targeted VOC control measures. Full article