Search Results (1,474)

This study analyzed the spatial distribution and seasonal variation characteristics of Volatile Organic Compounds (VOCs) at four sites (GS, GJ, BHS, and JN) representing different emission environments in Seoul and identified potential pollution sources using principal component analysis (PCA). The results showed that VOC concentrations were relatively high at the GS site, which is influenced by both industrial and traffic emissions, and at the JN site, characterized by heavy urban traffic, whereas the BHS site, representing a background area, exhibited the lowest concentrations, indicating clear spatial heterogeneity. Alkanes accounted for the largest proportion of VOCs at all sites, and low-molecular-weight alkanes as well as combustion-related compounds showed elevated concentrations during winter. In contrast, aromatic compounds exhibited site-specific seasonal patterns, with relatively higher concentrations observed during summer or autumn at some locations. The diurnal variation patterns displayed a bimodal distribution with concentration peaks during morning and evening rush hours, indicating the direct influence of traffic emissions. Furthermore, the T/B ratio and PCA results suggested that vehicle emissions and combustion sources were the dominant contributing factors (PC1) to ambient VOCs in Seoul, while non-road emission sources such as solvent use and industrial activities, characterized mainly by aromatic compounds, also contributed significantly (PC2). The findings of this study can serve as fundamental data for future quantitative source apportionment studies and the development of risk-based air quality management strategies for VOCs in Seoul. Full article

The transport sector plays a significant role in air pollution, and real-world emissions measurements are becoming increasingly important. In this study, emissions from a turbocharged, direct-injection gasoline internal combustion engine (ICE) vehicle and a port fuel injection (PFI) hybrid electric vehicle (HEV) were compared using a portable emissions measurement system (PEMS) under real-world driving conditions. The CO₂, CO, NO_x, and PN emissions of the two vehicles were measured in urban, rural, and motorway sections. HEV CO₂ emissions were ~20% lower than ICE emissions in the entire Real Driving Emissions (RDE) cycle, while in urban operation, they were almost 50% lower. PN emissions were lower for HEV in rural and motorway sections than for ICE, but significant PN peaks occurred during the early urban phase, attributable to the slower engine warm-up of the HEV. Machine learning analysis (Random Forest and Extra Trees Regressor) indicated that coolant temperature was the dominant driver of HEV PN emissions. The results indicate that powertrain characteristics and thermal management strongly influence real-world driving emissions, highlighting their importance for the further development of hybrid vehicles. Full article

PM_2.5 is a major air pollutant that threatens urban air quality and public health. Its concentration is influenced by both meteorological conditions and air pollutants, exhibiting complex nonlinear and temporal characteristics. Traditional statistical methods are limited in their ability to model complex relationships among environmental variables, while machine learning models still require improvements in hyperparameter optimization and interpretability. Therefore, developing an accurate and interpretable PM_2.5 estimation model remains an important research objective. This study used daily air-quality and meteorological data collected in Wuhan from 2016 to 2025 to develop six machine learning models: Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The Particle Swarm Optimization (PSO) algorithm was employed to optimize the hyperparameters of these models. By comparing the root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE) of each model on both the training and test sets, the PSO-MLP model was identified as the best-performing model. Furthermore, the Shapley Additive Explanations (SHAP) method was applied to perform both global and local interpretation analyses of the best-performing model. The results indicate that the PSO-MLP model achieved the highest estimation performance among all evaluated models, with an R² value of 0.746 on the test set. SHAP analysis revealed that CO, Temperature (Temp), and NO₂ were the most influential predictors, while all variables exhibited distinct nonlinear relationships with PM_2.5 concentration. These findings may contribute to PM_2.5 concentration estimation, air-quality management, and environmental decision-making. Full article

According to the “Technical Specification for Air Quality Testing in Archives Repositories,” air pollutants in archives can be categorized into exogenous and endogenous pollutants. Common exogenous pollutants include sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and hydrogen sulfide (H₂S), while endogenous pollutants mainly consist of formaldehyde (HCHO) and acetic acid (CH₃COOH). This study combines external electric field technology with density functional theory (DFT) and the B3LYP method to theoretically analyze the spectral characteristics and degradation mechanisms of these six pollutant gases. Molecular models of the six gases were constructed using Gaussian software. The configurations of five pollutant gas molecules (SO₂, NO₂, O₃, H₂S, and HCHO) were optimized using the B3LYP/6-31G(d) basis set, while the configuration of acetic acid was optimized using the B3LYP/3-21G basis set, yielding their stable structures and spectral information. The study found that characteristic peaks in the spectra shifted under the influence of an electric field. Additionally, by scanning the potential energy surfaces of selected molecular bonds under varying electric field strengths along specific directions, the required external electric field strengths for the degradation of the six common pollutant gases in archives were determined as follows: 0.1050 a.u. for SO₂, 0.0975 a.u. for NO₂, 0.0925 a.u. for O₃, 0.1000 a.u. for H₂S, 0.1500 a.u. for HCHO, and 0.0705 a.u. for CH₃COOH. The results clarify the degradation thresholds of these six pollutant gases under an external electric field. The findings indicate that acetic acid (0.0705 a.u.) and ozone (0.0925 a.u.) are highly sensitive to electric fields, while formaldehyde requires the strongest electric field (0.1500 a.u.) for degradation. These results provide a reference and theoretical foundation for electric field-assisted degradation technology targeting pollutant gases in archives. Full article

Xinjiang experiences frequent dust storms, posing significant challenges to regional ecological security and public health. Based on the China High-resolution and High-quality Near-surface Air Pollutants (CHAP) dataset and ground monitoring data, this paper adopts the Potential Source Contribution Function (PSCF) to analyze the spatiotemporal characteristics of atmospheric particulate matter across Xinjiang and typical cities and to identify potential source regions and contribution intensities. The results show that (1) PM_2.5 and PM₁₀ concentrations are elevated in southern Xinjiang but reduced in the north, and particulate pollution in most areas has generally decreased. (2) Northern Xinjiang cities have high PM_2.5 in winter, while southern Xinjiang cities keep persistently high PM₁₀ levels. (3) The PM_2.5/PM₁₀ ratio is above 0.35 in northern cities, where pollution is dominated by fine particles affected mainly by human activities; southern Xinjiang is dominated by coarse particles from natural sources. (4) Particulate matter in Urumqi mainly comes from the northern Tianshan Mountains, with winter WPSCF over 0.9. Pollutants in Kashgar originate from both long-distance cross-border dust transmission and local emissions. These findings are of great significance for the sustainable development of Xinjiang and urban agglomerations along the Belt and Road. Full article

Volatile organic compounds (VOCs) are major contributors to air pollution and pose significant risks to both environmental quality and human health. Biochar-based adsorption technology is an efficient and sustainable approach to VOCs removal. Herein, hybrid biochar was prepared from corn stover and municipal sewage sludge using the water vapor activation method, and its physicochemical characteristics and adsorption mechanisms for typical volatile organic compounds commonly produced during biomass-derived energy generation—such as methylbenzene, isopentane, and ethylene—were systematically investigated. The results show that hybrid biochar significantly outperformed single-source biochar, with its ability to adsorb methylbenzene, isopentane, and ethylene exceeding that of pure sludge biochar by 112.21%, 74.53%, and 66.72%, respectively, and surpassing pure corn stover biochar by 74.25%, 62.98%, and 55.25%, respectively. Competitive adsorption analysis indicated that the interaction strength between VOC molecules and the steam-treated hybrid carbon material was associated with their boiling points; compounds with higher boiling points tended to exhibit stronger affinity. This work provides an integrated waste utilization and pollution control strategy for VOCs removal. Full article

Urban air quality management has been playing a significant role due to its effects on public health and pollution characteristics of countries with constantly changing policies. Traditional approaches capture how much pollution is present but are unable to detect changes in the chemical character of the atmosphere, the relationships between co-emitted species, the balance of photochemical processing, and the combustion fingerprint of emission sources. This study introduces a framework that identifies and diagnoses such evolutions within the pollutants of the atmosphere. A chemistry-aware Variational Autoencoder is trained on 19 multivariate pollution features (7 raw concentrations, 5 chemical ratios, 7 temporal gradients) at London Marylebone Road (urban roadside) and North Kensington (urban background) from 2015 to 2019, and tested on 2022–2025. A four-method ensemble framework (VAE reconstruction error, reconstruction probability, Isolation Forest, and statistical Z-score) requires ≥3 agreement to identify high-confidence departed pollution states. Per-feature decomposition of the reconstruction probability diagnoses the chemical character of each departure. At the roadside site, 14.5% of post-COVID hours fall within departed states, dominated by the CO/NO_x combustion ratio (513.2) and the photostationary state proxy (391.4), chemical relationships rather than individual concentrations. This indicates that at the point of emission, London’s fleet modernisation and Ultra Low Emission Zone (ULEZ) have changed the combustion fingerprint and photochemical equilibrium. The same structural indicators are carried over during the COVID-19 lockdown; however, O₃ rises 3.2× during the pandemic period, reflecting suppressed NO titration. Conversely, at the urban background site, where the departures are driven by concentrations and boundary-layer trapping ($r=-0.659$), the combustion fingerprint of the atmosphere is invisible to detect (CO/NO_x$=-45.0$). These findings indicate that London’s emission landscape has undergone fundamental transformations over the past decade, and the consequences of ULEZ and similar interventions or greater impacts of pandemic-related events are non-homogeneously distributed across the relevant region. Full article

PM2.5 and near-surface O₃ compound pollution is a major challenge for further air quality improvement in the Yangtze River Delta (YRD). Despite research on the chemical coupling mechanisms and concentration co-variation between PM2.5 and O₃, the directional linkages of compound pollution events among cities and the network mechanisms underlying their formation remain unclear. Here, we identified PM2.5–O₃ compound pollution events for 41 YRD cities from 2015 to 2024 using city-year-specific P80 dual-threshold criteria. We then constructed annual directed synchronization networks based on event-leading relationships and used temporal exponential random graph models to identify the formation mechanisms of significant leading ties. PM2.5–O₃ compound pollution events in the YRD generally decreased during 2015–2024, with characteristics shifting from high frequency, persistence, and strong intercity linkage in the early stage to lower frequency, weaker intensity, and continued episodic fluctuations. Directed event networks exhibited a clear stage-dependent evolution: network density, total edge weight, reciprocity, and local closure were relatively high during 2015–2018, networks became markedly sparse during 2020–2022, and a partial rebound occurred after 2023. Spatial backbone analysis indicated reorganization of the dominant linkage structure, shifting from the Shanghai–southern Jiangsu–northern Zhejiang coastal core toward the northern Jiangsu, Anhui, and interprovincial corridors. Key node analysis further revealed a clear functional differentiation among cities, with some cities acting as potential leading sources, some as receiving nodes, and several non-traditional core cities serving as cross-regional bridges. Significant leading ties were jointly shaped by reciprocity, local closures, temporal memory, economic development, industrial structure, and digital governance. Therefore, as well as a problem of co-occurrence, PM2.5–O₃ compound pollution in the YRD is a cross-city event-network process characterized by directionality, stage-dependent evolution, and differentiated urban roles. This study provides empirical evidence for dynamic joint prevention and control based on event linkages, urban roles, and cross-city coordination. Full article

Background: Ambient air pollution is an established cardiovascular risk factor; however, its impact on long-term outcomes after coronary artery bypass grafting (CABG) remains insufficiently defined. We aimed to evaluate whether chronic exposure to air pollutants may influence long-term mortality following surgical revascularization. Methods: In this multicenter retrospective cohort study, 1033 consecutive patients undergoing CABG with BIMA (bilateral internal mammary arteries) grafting were analyzed with a median follow-up of 8.1 years. Individual exposure to nitrogen dioxide (NO₂), particulate matter ≤10 μm (PM₁₀), and ≤2.5 μm (PM_2.5) was estimated based on residential data. Multivariable Cox proportional hazards models were used to assess associations with long-term mortality. Model performance was evaluated using receiver operating characteristic (ROC) analysis, while incremental prognostic value was quantified using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Kaplan–Meier analyses were performed using data-driven thresholds and model-based risk stratification. Results: During follow-up, 220 deaths (21.1%) occurred. In multivariable analysis, both NO₂ and PM₁₀ were associated with increased mortality (NO₂: HR 2.70 per 10 μg/m³, 95% CI 2.03–3.59; PM₁₀: HR 2.73 per 10 μg/m³, 95% CI 1.94–3.83; both p < 0.001), whereas PM_2.5 was not significant. The clinical model demonstrated moderate discrimination (AUC 0.73), which improved significantly after inclusion of pollution variables (AUC 0.84; ΔAUC 0.11). Reclassification analysis showed substantial improvement (NRI 0.42, p < 0.001; IDI 0.11, p < 0.001). Kaplan–Meier analysis confirmed enhanced risk stratification, with a hazard ratio of 2.70 for the clinical model and 7.02 for the combined clinical and pollution model (both p < 0.001). Conclusions: In this retrospective cohort of patients undergoing CABG with BIMA grafting, higher long-term residential exposure to NO₂ and PM₁₀ was associated with greater all-cause mortality after adjustment for measured clinical and procedural factors. These findings support further investigation of environmental exposure as a prognostic marker in surgically treated coronary disease, pending external validation and more granular control for contextual confounding. These findings suggest that environmental exposure may represent a relevant component of long-term risk stratification, although confirmation in large-volume cohorts is required. Full article

Ambient air pollution remains a major public health concern, contributing to millions of premature deaths worldwide according to the World Health Organization. Regional air quality assessments are commonly performed using chemical-transport models that require substantial computational resources due to their detailed representation of atmospheric processes. This study explores the feasibility of applying the second-generation dispersion model URBAIR^® as a computationally efficient alternative for long-term regional air quality simulations. URBAIR^® was implemented for three European case studies within the DISTENDER project to simulate particulate matter (PM₁₀) and nitrogen dioxide (NO₂) concentrations for 2018 under different spatial and temporal resolutions. Model performance was assessed against background monitoring stations and compared across grid configurations. The results show that the model successfully reproduces annual mean concentration patterns, particularly in urban areas, with R² values ranging mostly between 0.2–0.6, RMSE between 16–36 µg.m⁻³, and mean bias from −8 to 5 µg.m⁻³, indicating overall acceptable statistical performance. Within the specific configurations evaluated in this study, increasing spatial resolution was not consistently associated with improved model performance. However, because spatial resolution covaried with other factors including meteorological temporal resolution, domain characteristics, and monitoring station density, the present analysis does not allow the independent effect of spatial resolution to be isolated. Moreover, a key limitation of the modeling approach is the absence of chemical transformation processes, which may affect the representation of secondary pollutants. Overall, the dispersion-based modeling framework substantially reduces computational demand and input complexity, proving suitable for long-term exposure and climate-related applications when annual average concentrations are the primary objective. In future studies, the modeling approach should be applied to other case studies to consolidate the findings of this exploratory work so that it may contribute to sustainability-oriented decision making by facilitating regional assessments of air quality and potential health impacts related to climate change. Full article

Microplastics (MPs) are persistent contaminants widely distributed across aquatic, terrestrial, and atmospheric environments. Previous reviews have shown that MPs can carry pollutants and change their environmental behavior, but the field is now broad enough that a simple repetition of sources, adsorption mechanisms, and toxicity would add limited value. This review therefore organizes MPs–pollutant–interactions as a connected chain from sources and environmental pathways to interaction mechanisms, biological effects, and management actions. It summarizes the major MPs input pathways and representative polymer types across water, soil, and air, and then explains how partitioning, surface adsorption, desorption, and pore filling control the binding and release of pollutants. The review further discusses how MPs properties, pollutant characteristics, pollutant mixtures, biofilms/plastisphere, and environmental factors jointly regulate these processes. In addition, it evaluates the consequences of MPs-pollutant coupling for pollutant mobility, bioavailability, biodegradation, bioaccumulation, trophic transfer, ecological toxicity, and human exposure. Finally, the review links these processes with practical management needs, including wastewater treatment, sludge reuse, agricultural plastic control, atmospheric monitoring, environmental education, and long-term risk assessment. By bringing these topics into one framework, this review provides a clearer basis for understanding and managing MPs-associated mixed pollution in environmental systems. Full article

Ventilation air methane (VAM) discharged from coal mines is considerable in volume, causing serious environmental pollution and energy resource waste. The methane concentration of raw VAM is generally lower than 0.3%, which greatly limits its efficient utilization. Blending low-cost solid fuels with VAM for regenerative oxidation is a practical and promising strategy to overcome the technical bottlenecks of VAM resource recovery. Clarifying the gas–solid two-phase flow behaviors inside millimeter-scale regenerative microchannels is critical for optimizing the process parameters and structural design of regenerative oxidation devices. In this work, numerical simulations are conducted using ANSYS Fluent 2022 R2 software to systematically explore the flow evolution characteristics and corresponding influencing factors of gas–solid two-phase flow in millimeter-scale microchannels to investigate three key objectives: (1) reveal the flow evolution characteristics of gas–solid two-phase flow in millimeter-scale microchannels along the flow direction; (2) quantify the effects of particle size and inlet velocity on particle deposition rate and deposition velocity; and (3) propose optimal operational parameter ranges to avoid microchannel blockage and improve particle transport performance. Along the flow direction, the near-wall velocity gradient gradually declines with the flow distance, while the thickness of the boundary layer grows continuously. Both particle deposition rate and deposition velocity are positively correlated with particle size. At an inlet velocity of 2 m/s, once the particle size exceeds 60 μm, the deposition rate and velocity rise markedly, and the particle outflow probability decreases significantly. For a fixed particle size, increasing flow velocity reduces both deposition rate and deposition velocity, which enhances the transport ability of pulverized coal particles and weakens wall adhesion. When the flow velocity is lower than 2.5 m/s, the outlet deposition rate exceeds 60%, and the particle deposition velocity rises sharply. Accordingly, maintaining flow velocity above 2.5 m/s and controlling particle size below 60 μm can effectively inhibit rapid particle deposition, improve particle transport performance, and avoid microchannel blockage. This study provides a theoretical basis and parameter reference for the structural and operational optimization of horizontal microchannels in pulverized coal-blended VAM regenerative oxidation systems. Full article

The potato chip processing (PCP) industry generates huge amounts of wastewater heavily polluted with organic matter and nutrients. The current treatment technology of PCP wastewater uses dissolved air flotation (DAF) and an activated sludge sequential batch reactor (SBR); both consume large amounts of chemicals and represent energy-intensive systems. This study explores the utilization of algae for the sustainable treatment of PCP wastewater, nutrient recovery, and algal biomass production. Conical flasks (1-L) and 6-L transparent plastic bottles were used as lab-scale algae photobioreactors (APBRs). Raw wastewater, an anaerobically pre-treated effluent and a DAF–SBR or shortly SBR effluent were used in the first, second, and third APBR. Three feed volumes from each source (150 mL, 300 mL, and 500 mL for first and second APBR and 400 mL, 600 mL, and 800 mL for third APBR) to a fixed volume of algal seed (200 mL) were tested to select the optimal feed volume and harvest time using a 1-L APBR. System performance and impact of water characteristics on quantity and quality of algal biomass were explored at pre-selected feed volume and harvest time in 6-L APBRs. All experiments were carried out in a growth chamber with continuous light (148.75 μmol.m⁻².S⁻¹). The results showed that 150 mL is the optimal feed volume for the first and second APBR at 10 days and 9 days growth cycles. An amount of 500 mL and 6 days were selected as the optimal feed volume and growth cycle for the third APBR. The average dry biomass yields at the pre-selected optimal conditions were 65.3 ± 11.4, 69.9 ± 12.0, and 100.6 ± 11.7 mg/L.d in the first, second, and third APBR, respectively. The first APBR achieved removals of 99.2 ± 0.4%, 98.7 ± 0.8%, 89.1 ± 4.3%, and 97.5 ± 1.4% for turbidity, COD, TKN, and TP, respectively, on average. Corresponding removal in the second APBR is 97.6 ± 2.6%, 91.6 ± 7.5%, 93.6 ± 4.5%, and 96.1 ± 1.4%, respectively, while the third APBR achieved 98.5%, 76.2%, and 97.0%, respectively. Additionally, the results of protein content and amino acids profiles indicate significant impacts of feed water quality on both parameters. The protein content was 30.64%, 32.53%, and 35.65% in the first, second, and third APBR, respectively. Similarly, the amino acids profile indicated a significant higher percentage of the amino acids in the third reactor compared with the first and second reactor. Full article

This study investigates the relationship between weather conditions (temperature, humidity), air pollutants (PM_2.5, PM₁₀, and CO), and photovoltaic (PV) degradation characteristics using location-specific machine learning frameworks. A data augmentation technique was employed to enhance the predictive modeling datasets. The research evaluates four machine learning models: AdaBoost, Gradient Boosting, Decision Tree, and Random Forest. We found strong regression analysis values using the addressed machine learning models. Furthermore, feature importance analysis reveals that PM_2.5 has the most significant impact on PV module degradation. Full article

The Pearl River Delta region suffers from congestion in the urban road network, noise, air pollution, and other “urban diseases”. Vigorously developing inland water transportation can greatly alleviate these “urban diseases”. However, it is difficult to take advantage of the inland waterway transportation cost advantages due to the Pearl River Delta’s short haul distance characteristics. In recent business practice, a novel, environment-friendly, and competitiveness-enhanced inland waterway transportation mode has emerged in the area, called the leftover-cargo mode in this paper. This mode is composed of first-tier (big companies) and second-tier (small companies) inland barge companies, which establish a cooperative relationship and jointly meet the needs of shippers and can lead to a modal shift from inland truck to inland waterway transportation. In real practice, the pricing methods of this novel mode still rely on experience. We propose four pricing game theory models based on channel leadership in order to investigate how decision-making impacts the pricing and income of the two-tier companies. We find that, if the market price ceiling is low, second-tier inland barge companies always benefit more than first-tier companies, which is very interesting and counter to the existing literature. These findings offer pricing insights into economically viable leftover-cargo cooperation and its role in supporting sustainable road-to-waterway freight modal shift in the Pearl River Delta. Full article