Search Results (2,051)

The accurate and effective prediction of PM2.5 concentrations is crucial for mitigating air pollution, improving environmental quality, and safeguarding public health. To address the challenge of strong temporal correlations in PM2.5 concentration forecasting, this paper proposes a novel hybrid model that integrates the Particle Optimization Algorithm (POA) and Variational Mode Decomposition (VMD) with the Long Short-Term Memory (LSTM) network. First, POA is employed to optimize VMD by adaptively determining the optimal parameter combination [k, α], enabling the decomposition of the original PM2.5 time series into subcomponents while reducing data noise. Subsequently, an LSTM model is constructed to predict each subcomponent individually, and the predictions are aggregated to derive hourly PM2.5 concentration forecasts. Empirical analysis using datasets from Beijing, Tianjin, and Tangshan demonstrates the following key findings: (1) LSTM outperforms traditional machine learning models in time series forecasting. (2) The proposed model exhibits superior effectiveness and robustness, achieving optimal performance metrics (e.g., MAE: 0.7183, RMSE: 0.8807, MAPE: 4.01%, R²: 99.78%) in comparative experiments, as exemplified by the Beijing dataset. (3) The integration of POA with serial decomposition techniques effectively handles highly volatile and nonlinear data. This model provides a novel and reliable tool for PM2.5 concentration prediction, offering significant benefits for governmental decision-making and public awareness. Full article

The two-spotted spider mite, Tetranychus urticae, is one of the polyphagous pests of several crops and forestry, resistant to numerous conventional chemicals. Due to the negative side effects of harmful chemical pesticides, such as environmental pollution, and risks to human health, the introduction of effective and low-risk alternatives is essential. The promising pesticidal effects of essential oils (EOs) isolated from Artemisia annua have been documented in recent studies. In the present study, the acaricidal effects of an A. annua EO, along with its two dominant monoterpenoids, 1,8-cineole and camphor, were investigated against adults of T. urticae. Artemisia annua EO, 1,8-cineole, and camphor, with 24 h-LC₅₀ values of 0.289, 0.533, and 0.64 µL/L air, respectively, had significant toxicity by fumigation against T. urticae adults. Along with lethality, A. annua EO and monoterpenoids had significant inhibitory effects on the activity of detoxifying enzymes, including α- and β-esterases, glutathione S-transferases, and cytochrome P-450 monooxygenase. According to the findings of the present study, A. annua EO and its two dominant monoterpenoids, 1,8-cineole and camphor, with significant toxicity and inhibitory effects on detoxifying enzymes, can be introduced as available, effective, and eco-friendly acaricides in the management of T. urticae. Full article

With the growing severity of environmental pollution, people are paying increasing attention to their health. However, naturally occurring wood with health benefits and applications in human healthcare is still scarce. Natural wood exhibits a limited negative oxygen ion release capacity, and this release has a short duration, failing to meet practical application requirements. This study innovatively developed a humidity-responsive, healthy wood material with a high negative oxygen ion release capacity based on fast-growing poplar. Through vacuum cyclic impregnation technology, hexagonal stone powder was infused into the pores of poplar wood, endowing it with the ability to continuously release negative oxygen ions. The healthy wood demonstrated a static average negative oxygen ion release rate of 537 ions/cm³ (peaking at 617 ions/cm³) and a dynamic average release rate of 3,170 ions/cm³ (peaking at 10,590 ions/cm³). The results showed that the particle size of hexagonal stone powder in suspension was influenced by the dispersants and dispersion processes. The composite dispersion process demonstrated optimal performance when using 0.5 wt% silane coupling agent γ-(methacryloxy)propyltrimethoxysilane (KH570), achieving the smallest particle size of 8.93 μm. The healthy wood demonstrated excellent impregnation performance, with a weight gain exceeding 14.61% and a liquid absorption rate surpassing 165.18%. The optimal impregnation cycle for vacuum circulation technology was determined to be six cycles, regardless of the type of dispersant. Compared with poplar wood, the hygroscopic swelling rate of healthy wood was lower, especially in PEG-treated samples, where the tangential, radial, longitudinal, and volumetric swelling rates decreased by 70.93%, 71.67%, 69.41%, and 71.35%, respectively. Combining hexagonal stone powder with fast-growing poplar wood can effectively enhance the release of negative oxygen ions. The static average release of negative oxygen ions from healthy wood is 1.44 times that of untreated hexagonal stone powder, and the dynamic release reaches 2 to 3 times the concentration of negative oxygen ions specified by national fresh air standards. The water-responsive mechanism revealed that negative oxygen ion release surged when ambient humidity exceeded 70%. This work proposes a sustainable and effective method to prepare healthy wood with permanent negative oxygen ion release capability. It demonstrates great potential for improving indoor air quality and enhancing human health. Full article

Microplastics are pervasive environmental pollutants that pose risks to human health through ingestion and inhalation. This review synthesizes current practices to reduce exposure and toxicity by examining major exposure routes and dietary interventions. More than 130 papers were analyzed to achieve this aim. The findings show that microplastics contaminate a wide range of food products, with particular concern over seafood, drinking water, plastic-packaged foods, paper cups, and tea filter bags. Inhalation exposure is mainly linked to indoor air quality and smoking, while dermal contact poses minimal risk, though the release of additives from plastics onto the skin remains an area of concern. Recommended strategies to reduce dietary exposure include consuming only muscle parts of seafood, moderating intake of high-risk items like anchovies and mollusks, limiting canned seafood liquids, and purging mussels in clean water before consumption. Avoiding plastic containers, especially for hot food or microwaving, using wooden cutting boards, paper tea bags, and opting for tap or filtered water over bottled water are also advised. To mitigate inhalation exposure, the use of air filters with HyperHEPA systems, improved ventilation, regular vacuuming, and the reduction of smoking are recommended. While antioxidant supplementation shows potential in reducing microplastic toxicity, further research is needed to confirm its effectiveness. This review provides practical, evidence-based recommendations for minimizing daily microplastic exposure. Full article

Educational buildings, including schools, nurseries and universities, face stricter regulation and design control on indoor air quality (IAQ) and thermal conditions than other built environments, as these may affect children’s health and wellbeing. In this scoping review, wide-ranging health, performance, and absenteeism consequences of poor—and benefits of good—IAQ and thermal conditions are evaluated, focusing on source control, ventilation and air purification interventions. Economic impacts of interventions in educational buildings have been evaluated to enable the assessment of tangible building-related costs and savings, alongside less easily quantifiable improvements in educational attainment and reduced healthcare. Key recommendations are provided to assist decision makers in pathways to provide clean air, at an optimal temperature for students’ learning and health outcomes. Although the role of educational buildings can be challenging to isolate from other socio-economic confounders, secondary short- and long-term impacts on attainment and absenteeism have been demonstrated from the health effects associated with various pollutants. Sometimes overlooked, source control and repairing existing damage can be important cost-effective methods in minimising generation and preventing ingress of pollutants. Existing ventilation standards are often not met, even when mechanical and hybrid ventilation systems are already in place, but can often be achieved with a fraction of a typical school budget through operational and maintenance improvements, and small-scale air-cleaning and ventilation technologies, where necessary. Full article

Combustion of aviation jet fuel emits a complex mixture of pollutants linked to adverse health outcomes among airport personnel and nearby communities. While epidemiological studies showed the detrimental effects of aviation-derived air pollutants on human health, the molecular mechanisms of the interactions of these pollutants with cellular biomolecules like proteins that drive the adverse health effects remain poorly understood. In this study, we performed molecular docking simulations of 272 pollutant–protein complexes using AutoDock Vina 1.2.7 to characterize the binding strength of the pollutants with the selected proteins. We selected 34 aviation-derived pollutants that constitute three chemical categories of pollutants: volatile organic compounds (VOCs), polyaromatic hydrocarbons (PAHs), and organophosphate esters (OPEs). Each pollutant was docked to eight proteins that play critical roles in endocrine, metabolic, transport, and neurophysiological functions, where functional disruption is implicated in disease. The effect of binding of multiple pollutants was analyzed. Our results indicate that aliphatic and monoaromatic VOCs display low (<6 kcal/mol) binding affinities while PAHs and organophosphate esters exhibit strong (>7 kcal/mol) binding affinities. Furthermore, the binding strength of PAHs exhibits a positive correlation with the increasing number of aromatic rings in the pollutants, ranging from nearly 7 kcal/mol for two aromatic rings to more than 15 kcal/mol for five aromatic rings. Analysis of intermolecular interactions showed that these interactions are predominantly stabilized by hydrophobic, pi-stacking, and hydrogen bonding interactions. Simultaneous docking of multiple pollutants revealed the increased binding strength of the resulting complexes, highlighting the detrimental effect of exposure to pollutant mixtures found in ambient air near airports. We provide a priority list of pollutants that regulatory authorities can use to further develop targeted mitigation strategies to protect the vulnerable personnel and communities near airports. Full article

Long-term source apportionment of PM_2.5 during high-pollution periods is essential for achieving sustained reductions in both PM_2.5 levels and their health impacts. This study conducted PM_2.5 sampling in Shenzhen from January to March over the years 2021–2024 to investigate the long-term impact of coronavirus disease 2019 and the short-term impact of the Spring Festival on PM_2.5 levels. The measured average PM_2.5 concentration during the research period was 22.5 μg/m³, with organic matter (OM) being the dominant component. Vehicle emissions, secondary sulfate, secondary nitrate, and secondary organic aerosol were identified by receptor model as the primary sources of PM_2.5 during the observational periods. The pandemic led to a decrease of between 30% and 50% in the contributions of most anthropogenic sources in 2022 compared to 2021, followed by a rebound. PM_2.5 levels in January–March 2024 dropped by 1.4 μg/m³ compared to 2021, mainly due to reduced vehicle emissions, secondary sulfate, fugitive dust, biomass burning, and industrial emissions, reflecting Shenzhen’s and nearby cities’ effective control measures. However, secondary nitrate and fireworks-related emissions rose significantly. During the Spring Festival, PM_2.5 concentrations were 23% lower than before the festival, but the contributions of fireworks burning exhibited a marked increase in both 2023 and 2024. Specifically, during intense peak events, fireworks burning triggered sharp, short-term spikes in characteristic metal concentrations, accounting for over 50% of PM_2.5 on those peak days. In the future, strict control over vehicle emissions and enhanced management of fireworks burning during special periods like the Spring Festival are necessary to reduce PM_2.5 concentration and improve air quality. Full article

According to the World Health Organization (WHO), indoor air quality (IAQ) is becoming a serious global concern due to its significant impact on human health. However, not all relevant health parameters are currently regulated. For example, particle number concentration (PNC) and its associated carbonaceous species, such as black carbon (BC), which are classified as carcinogenic by the International Agency for Research on Cancer (IARC), are not currently regulated. Compared with IAQ studies in other types of buildings, studies focusing on IAQ in hospitals or other healthcare facilities are scarce. Therefore, this study aims to evaluate the impact of these outdoor pollutants, among others, on the indoor environment of a hospital under different atmospheric conditions. To identify the seasonal influence, two different periods of two consecutive seasons (summer 2020 and winter 2021) were selected for the measurements. Regulated pollutants (NO, NO₂, O₃, PM₁₀, and PM_2.5) and nonregulated pollutants (PM₁, PNC, and equivalent BC (eBC)) in outdoor air were simultaneously measured indoor and outdoor. This study also investigated the impact of indoor activities on indoor air quality. In the absence of indoor activities, outdoor sources significantly contribute to indoor traffic-related pollutants. Indoor and outdoor (I-O) measurements showed similar behavior, but indoor concentrations were lower, with peak levels delayed by up to two hours. Seasonal variations in indoor/outdoor ($\mathrm{I}/\mathrm{O}$) ratios were lower for particles than for associated gaseous pollutants. Particle infiltration depended on particle size, with it being higher the smaller the particle size. Indoor activities also significantly affected indoor pollutants. PM_x (especially PM₁₀ and PM_2.5) concentrations were mainly modulated by walking-induced particle resuspension. Vertical eBC profiles indicated a relatively well-mixed environment. Ventilation through open windows rapidly altered indoor air quality. Outdoor-dominant pollutants (PNC, eBC, and NO_X) had $\mathrm{I}/\mathrm{O}$ ratios ≥ 1. Staying in the room with an open window had a synergistic effect, increasing the $\mathrm{I}/\mathrm{O}$ ratios for all pollutants. Higher I/O ratios were associated with turbulent outdoor conditions in both unoccupied and occupied conditions. Statistically significant differences were observed between stable (TKE ≤ 1 m² s⁻²) and unstable (TKE > 1 m² s⁻²) conditions, except for NO₂ in summer. This finding was particularly significant when the wind direction was westerly or easterly during unstable conditions. The results of this study highlight the importance of understanding the behavior of indoor particulate matter and related pollutants. These pollutants are highly variable, and knowledge about them is crucial for determining their health effects, particularly in public buildings such as hospitals, where information on IAQ is often limited. More measurement data is particularly important for further research into I-O transport mechanisms, which are essential for developing preventive measures and improving IAQ. Full article

Air pollution, particularly fine (PM_2.5) and coarse (PM₁₀) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. A five-year dataset (2020–2024), comprising 20 meteorological and pollution-related variables recorded by four air quality monitoring stations, was analyzed. The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. A deep Nonlinear AutoRegressive Moving Average with eXogenous inputs (Deep-NARMAX) model was employed for multi-step-ahead forecasting. The best-performing models achieved R² values of 0.85 for PM_2.5 and 0.89 for PM₁₀, with low RMSE and MAPE scores, demonstrating high accuracy and generalizability. The GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. These results highlight the potential of hybrid AI models for air quality management and provide a scalable framework for urban pollution monitoring, predicting, and forecasting. Full article

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

The demand for rapid, portable, and cost-effective analytical tools has driven advances in smartphone-based microfluidic sensors. By combining microfluidic precision with the accessibility and processing power of smartphones, these devices offer real-time and on-site diagnostic capabilities. This review explores recent developments in smartphone-integrated microfluidic sensors, focusing on their design, fabrication, smartphone integration, and analytical functions with the applications in forensic science, agriculture, and environmental monitoring. In forensic science, these sensors provide fast, field-based alternatives to traditional lab methods for detecting substances like DNA, drugs, and explosives, improving investigation efficiency. In agriculture, they support precision farming by enabling on-demand analysis of soil nutrients, water quality, and plant health, enhancing crop management. In environmental monitoring, these sensors allow the timely detection of pollutants in air, water, and soil, enabling quicker responses to hazards. Their portability and user-friendliness make them particularly valuable in resource-limited settings. Overall, this review highlights the transformative potential of smartphone-based microfluidic sensors in enabling accessible, real-time diagnostics across multiple disciplines. 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

The occurrence of haze pollution significantly deteriorates air quality and threatens human health, yet persistent knowledge gaps in real-time source apportionment of fine particulate matter (PM_2.5) hinder sustained improvements in atmospheric pollution conditions. Thus, this study employed single-particle aerosol mass spectrometry (SPAMS) to investigate PM_2.5 sources and dynamics during winter haze episodes in Yinchuan, Northwest China. Results showed that the average PM_2.5 concentration was 57 μg·m⁻³, peaking at 218 μg·m⁻³. PM_2.5 was dominated by organic carbon (OC, 17.3%), mixed carbonaceous particles (ECOC, 17.0%), and elemental carbon (EC, 14.3%). The primary sources were coal combustion (26.4%), fugitive dust (25.8%), and vehicle emissions (19.1%). Residential coal burning dominated coal emissions (80.9%), highlighting inefficient decentralized heating. Source contributions showed distinct diurnal patterns: coal combustion peaked nocturnally (29.3% at 09:00) due to heating and inversions, fugitive dust rose at night (28.6% at 19:00) from construction and low winds, and vehicle emissions aligned with traffic (17.5% at 07:00). Haze episodes were driven by synergistic increases in local coal (+4.0%), dust (+2.7%), and vehicle (+2.1%) emissions, compounded by regional transport (10.1–36.7%) of aged particles from northwestern zones. Fugitive dust correlated with sulfur dioxide (SO₂) and ozone (O₃) (p < 0.01), suggesting roles as carriers and reactive interfaces. Findings confirm local emission dominance with spatiotemporal heterogeneity and regional transport influence. SPAMS effectively resolved short-term pollution dynamics, providing critical insights for targeted air quality management in arid regions. Full article

This study investigates the health impacts of air pollution in the Greater Athens Area (GAA), Greece, by estimating the Relative Risk (RR) of hospital admissions (HA) for cardiovascular (CVD) and respiratory diseases (RD) from 2018 to 2020. The analysis focuses on daily exceedances of key air pollutants—PM₁₀, O₃, and NO₂—based on the “Fair” threshold and above, as defined by the European Union Air Quality Index (EU AQI). Data from ten monitoring stations operated by the Ministry of Environment and Energy were spatially matched with six hospitals across the GAA. A Distributed Lag Non-linear Model (DLNM) was employed to capture both the delayed and non-linear exposure–response (ER) relationships between pollutant exceedances and daily HA. Additionally, the synergistic effects of pollutant interactions were assessed to provide a more comprehensive understanding of cumulative health risks. The combined exposure term showed a peak RR of 1.49 (95% CI: 0.79–2.78), indicating a notable amplification of risk when multiple pollutants exceed thresholds simultaneously. The study utilizes R for data processing and statistical modeling. Findings aim to inform public health strategies by identifying critical exposure thresholds and time-lagged effects, ultimately supporting targeted interventions in urban environments experiencing air quality challenges. 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