Search Results (851)

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article

As part of efforts to support the transition toward a zero-carbon future, this research evaluates how the use of natural gas and liquefied petroleum gas under lean burn conditions affects the energy efficiency and environmental outcomes of a diesel engine that has been retrofitted to operate with spark ignition. The assessment of the ecological potential of these low-carbon gaseous fuels was performed at the engine test bed at optimum spark advance set from the condition of achieving maximum brake thermal efficiency (i.e., lowest carbon dioxide emission, CO₂). The results found with lean mixtures are compared to those obtained under stoichiometric conditions, as well as to those from a commercial gasoline engine of comparable size, equally operated at stoichiometry. With lean burning, a clear improvement is observed for all operating points in terms of brake thermal efficiency with respect to the stoichiometric operation. The results highlight a slightly greater improvement when operating with natural gas lean mixtures: between (1.35 and 2.35) percentage points gained in this case, compared to (1.15–2.10) percentage points gained in the case of liquefied petroleum gas. As for CO₂, a maximum 28% reduction when using natural gas is achieved with lean operation with respect to the commercial gasoline engine. Using lean mixtures also brings an important reduction in the engine-out pollutants (carbon monoxide, nitric oxides and particulate number). However, with respect to stoichiometric operation, cyclic variability of the prototype degrades with lean burning but remains lower than one of the baseline commercial gasoline engines. Full article

This study investigates the vertical variation and temporal characteristics and indicates the sources of black carbon (BC) and brown carbon (BrC) within particulate matter fraction PM₁ during light (November–December 2024) and heavy (January–February 2025) haze episodes in Bangkok, Thailand, a topic where data are still limited data regarding Southeast Asian megacities. Continuous measurements were conducted at 30 and 110 m above ground level, together with particle size distribution measurement, micrometeorological observations, and backward air mass trajectory analysis. During the haze periods, the highest particle number concentrations occurred in the 0.3–0.4 µm size range, indicating dominant contributions from combustion-related emissions and secondary aerosol formation. Mean PM₁ mass concentrations during the heavy haze episodes were more than 2.5 times higher than those during light haze. BC concentrations increased substantially during heavy haze, while the BC fraction of PM₁ remained relatively constant (~10%). In contrast, the BrC fraction reached nearly 20%, reflecting an increasing influence of biomass burning emissions associated with regional transport. Combined analyses of BC/BrC relationships, wind-direction dependence, and air mass trajectories demonstrate mixed contributions from local fossil fuel combustion and long-range transport of biomass burning aerosols during severe haze events. Full article

Particle deposition, rebound, and adhesion on rough surfaces play a crucial role in a wide range of powder handling, aerosol transport, and fouling-related processes. However, the underlying mechanisms governing single-particle interactions with rough surfaces, particularly those with complex surface morphologies, remain insufficiently understood. In this work, the deposition and elastic rebound behavior of an individual particle impacting superhydrophobic surfaces with ribbed and randomly distributed roughness structures are systematically investigated through a combined experimental and numerical approach. A coupled Lattice Boltzmann Method (LBM) and Discrete Particle Model (DPM) was developed, in which a new particle–surface contact model is proposed to account for adhesion, elastic deformation, and localized roughness effects through multi-node interactions. Randomly distributed rough surfaces are reconstructed using a Fast Fourier Transform (FFT)-based method, and single-particle impact experiments are conducted to validate the numerical predictions. Good agreement is achieved between simulated and measured values, with a relative error for the maximum rebound height of only 5.9% and a peak velocity deviation prior to impact of approximately 5.4%. Parametric analyses demonstrate that particle diameter, Young’s modulus, surface energy, surface roughness morphology, and flow Reynolds number all influence particle deposition outcomes. Larger particles exhibit significantly higher rebound heights due to increased stored elastic energy; specifically, when particle size increases from 20 μm to 100 μm, the maximum rebound height increases by a factor of 2.1. In contrast, smaller particles are more prone to adhesion after repeated impacts. The rebound height of particles decreases as surface energy increases. When surface energy rises from 0.01 J/m² to 0.05 J/m², rebound height drops from 53.65% to 38.66%. At 0.5 J/m², particles adhere immediately. Compared with ribbed surfaces, randomly distributed rough surfaces promote particle rebound by reducing effective contact area and inducing complex impact orientations. Particle rebound behavior is primarily governed by particle diameter, while material properties such as Young’s modulus and surface energy exhibit secondary and nonlinear effects. The proposed model provides a validated and transferable framework for analyzing particle–surface interactions on rough surfaces and offers physical insights relevant to the control of particle deposition in powder and particulate systems. Full article

In recent years, more and more studies have been published on the impact of air pollution on the increase in the incidence of type 1 diabetes mellitus (T1DM) in children and adolescents. To confirm this, we attempted to show whether there are differences between the impact of air pollution in 2017–2018 compared to the impact of air pollution during the lockdown period, i.e., 2020–2021, and its potential relationship with the incidence rates of new cases of T1DM. Methods: We obtained the number of new cases of T1DM in 2017–2018 and 2020–2021 in the Lublin Voivodeship. Data on the annual average concentrations of nitrogen dioxide (NO₂), nitric oxides (NO_x), sulphur dioxide (SO₂) and particulate matter (PM₁₀ and PM_2.5) were obtained from Annual Air Quality Assessment reports from 2017–2018 and 2020–2021, made available by the Provincial Inspectorate of Environmental Protection (WIOS) in Lublin. Results: In 2017–2018, air pollution in the entire Lublin Voivodeship was higher than during the lockdown period, i.e., 2020–2021. Moreover, in 2017 and 2018 in the Lublin Voivodeship, strong statistically significant positive correlations were found between NO₂ and PM_2.5 concentrations and the occurrence of T1DM in children. Conclusions: The research results indicate that air pollution is one of the factors that may suggest a potential association with the development of T1DM. Therefore, every effort should be made to minimize air pollution, which will reduce the risk of developing T1DM and other diseases. Full article

Airborne particulate matter with a diameter of <10 μm (PM₁₀) can damage the corneal epithelium by inducing oxidative stress, disrupting the NRF2 antioxidant pathway, and triggering epithelial barrier dysfunction and inflammation. However, the role of mitochondria in mediating PM₁₀-induced damage remains unexplored. This study investigated the impact of PM₁₀ on mitochondrial homeostasis in both immortalized human corneal epithelial cells (HCE-2) and the mouse corneal epithelium, as well as the protective effects of SKQ1. For in vivo assessment, female C57BL/6 mice were exposed to either control air or PM₁₀ (±SKQ1) in a whole-body exposure chamber for 2 weeks (3 h/day, 5 days/week, with weekends off). In vitro, HCE-2 cells were exposed to 100 μg/mL PM₁₀ (±SKQ1) for 24 h, and mitochondrial function and morphology were evaluated. In vitro, PM₁₀ significantly impaired mitochondrial function by reducing basal, maximal, and ATP-linked respiration; reserve capacity; and coupling efficiency compared to the control and SKQ1 groups. PM₁₀ also downregulated mitofusin1 (MFN1) and optic atrophy1 (OPA1) and upregulated dynamin-related protein1 (DRP1) and mitochondrial fission protein1 (FIS1) in HCE-2 cells. In addition, PM₁₀ exposure significantly decreased the mitochondrial membrane potential; mitochondrial DNA copy number; and cytochrome c oxidase subunit 4 isoform 1 (COX4i1), mitochondrial transcription factor A (TFAM), and peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α) levels. SKQ1 pre-treatment significantly attenuated these effects. In vivo, PM₁₀ exposure significantly decreased the levels of MFN1, TFAM, COX4i1, and superoxide dismutase (SOD2), whereas SKQ1 treatment significantly reversed these effects. Overall, these findings demonstrate that PM₁₀ exposure induces mitochondrial fragmentation, disrupts mitochondrial biogenesis and quality control, and reduces mitochondrial respiration, resulting in mitochondrial dysfunction. SKQ1 effectively reversed these changes, suggesting its potential as a therapeutic strategy to protect corneal epithelial cells from PM₁₀-induced mitochondrial damage. Full article

The impact of air pollution on human health remains a critical issue. This study investigates the concentrations of PM_2.5 and PM_2.5–10 and translates measured exposure concentrations to internal human dose using the Exposure Dose Model 2 (ExDoM2). The cities analyzed (Poznań, Wrocław) were selected based on their demographic and functional significance and the structure of dominant emission sources. These are large academic centers with a significant influx of residents, leading to the seasonal increase in the number of people exposed to air pollution. The total deposited doses of PM_2.5 in the human respiratory tract (HRT) for adult males varied seasonally, with the highest dose recorded in winter and autumn equal to 180 µg in Wrocław and Poznań, and the lowest in spring and summer equal to 30 µg and 65 µg in Wrocław and Poznań, respectively. These findings highlight the significant impact of seasonal variability on exposure to particulate matter and its potential health implications. In particular, the deposited doses of particulate matter in Wrocław and Poznań were found to be within a similar range during certain seasons, indicating comparable urban exposure levels. During the heating season, municipal and residential emissions related to the combustion of solid fuels in individual heat sources play a key role, while during the non-heating season, traffic emissions and secondary particulate matter resuspension are more significant. Further research is required to determine the extent to which these similarities reflect shared emission sources or meteorological conditions. Full article

Nowadays, air quality is a major issue, especially in large cities. Apart from air pollution, particulate matter (PM), especially PM2.5, poses serious health risks to individuals with respiratory conditions. Accurate forecasting of PM levels is crucial to warn vulnerable populations and reduce exposure. Machine learning models can effectively predict PM concentrations based on historical data and barometric conditions such as temperature and humidity. Such predictions can support timely public health interventions and environmental policy decisions. The selection of the optimal machine learning model for time series forecasting requires a careful balance between predictive accuracy and computational efficiency. This study evaluates a number of widely used models, such as Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM (CNN–LSTM), Extreme Gradient Boosting (XGB/HistGradientBoosting), and hybrid approaches (LSTM embeddings + RF), in the context of time series forecasting for particulate matter (PM) concentrations. Performance is assessed using three key error metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Scaled Error (MASE). Additionally, the computational demands and development complexity of each model are analyzed. The overall results are of great interest for each application model, and in more detail, it is shown that the best compromise between accuracy and efficiency can be achieved, while a corresponding prediction model with satisfactory predictive performance can be implemented. The results show that CNN–LSTM and hybrid approaches provide high accuracy, while tree-based models are computationally efficient, offering practical options for real-time forecasting systems. Full article

The complexity of modern vehicle control systems, the increasing diversity of powertrain and exhaust aftertreatment applications, and the need for shortened development times require innovative approaches towards calibration. This paper presents an experimental, analytical, and modeling study of particulate filter (commonly called DPF—diesel particulate filter) in a diesel hybrid vehicle where models have been developed to simulate test data, replacing the requirement of numerous tests on testbed or on the road with system simulations and offline parameter optimisation techniques. A soot estimation model has been developed based on the operation of the engine including its transient response, and the thermal–chemical behaviour of the DPF. A methodology has been developed to optimize the calibratable maps and parameters within this model. The results show that the proposed method improves the accuracy of soot estimation in the engine transient operation and avoids a large number of experimental tests required in traditional calibration methods. Modern automotive manufacturers face regulatory compliance requirements ensuring emission standards across diverse real driving emission (RDE) boundary conditions encompassing route characteristics, driving dynamics, and ambient environmental variables throughout vehicles’ operational lifetime. The soot load in the DPF and the DPF regeneration frequency can massively impact the tailpipe NO_x emissions and overall fuel consumption, so it is key to accurately estimate the soot accumulation in all operating conditions. This means testing and validating calibration in each possible scenario and so needs an enormous number of tests on testbed and on the road. These tests, however, can be replaced with system simulations and offline calibration if we have a robust model for the system, as described in the following parts of this paper. Full article

In intensive pig production systems, limited space and lack of enrichment materials (EMs) restrict natural behaviors, inducing chronic stress and impairing welfare and health. Conventional EMs such as straw and sawdust improve comfort but increase NH₃ and particulate emissions and hinder manure management on slatted floors. This study compared rice-straw silage (RS), sawdust (SD), and sling belt (SB) as EMs for growing-finishing pigs to evaluate their effects on growth performance, behavior, body lesions, cleanliness score of body, and pen air quality. A total of 344 crossbred pigs ([Landrace × Yorkshire] × Duroc, 30.5 ± 3.10 kg) were randomly allocated to four treatments: Control, 50% slatted and 50% solid flooring; RS, 100% solid flooring with a 7-cm layer of RS; SD, 100% solid flooring with a 7-cm layer of SD; SB, 50% slatted and 50% solid flooring with 10 SBs (1.5 m long and 75 mm wide). At week 10, the RS pigs had the lowest body weight. At week 0, the RS and SD pigs exhibited more positive behaviors, although the SD pigs also showed the highest number of injurious interactions at week 3. Between weeks 0 and 5, the SD pigs spent less time lateral lying and more time sternal lying, while during weeks 8–11, sitting was more prevalent. Both RS and SD groups exhibited lower cleanliness scores at week 6 and higher NH₃ and CO₂ levels at week 10. In conclusion, bedding materials such as RS and SD promoted positive behaviors during the early phase; however, prolonged use without adequate management impaired hygiene, air quality, resting behavior, and growth performance. These findings highlight the importance of the appropriate selection and management of EMs in intensive pig production systems. Full article

This study aims to address the lack of systematic research on how air filter cleaning affects pollutant distribution in subway stations, an urgent issue for ensuring indoor air quality in enclosed underground transit environments. Taking a representative subway line in Xi’an, China, as the research object, we monitored and analyzed pollutant concentrations (including particulate matter and microorganisms) at air supply outlets (near, semi-far, and far from the air filter) and different locations in a single station hall before and after filter cleaning, with the goal of clarifying the cleaning effect and its variation with distance from the air supply. The results show that, before cleaning, pollutant distribution exhibits distinct spatial characteristics: PM₁₀ concentration is 16.7% higher at outlets closer to the filter, while PM_2.5 and PM_1.0 concentrations are 22.4% and 19.7% higher at distant outlets, respectively. After cleaning, all pollutants are significantly reduced: PM₁₀ at near outlets decreases by 25.9%, PM_2.5 and PM_1.0 at far outlets drop by 44.2% and 42.7%, respectively, and the total numbers of bacteria and fungi in the air supply meet national hygiene standards. A key novel finding is that cleaning has differentiated effects on particle sizes across locations; it is more effective for larger particles at near outlets and for smaller particles at far outlets. This research not only quantifies the purification effect of filter cleaning but also reveals the spatial variation law of its impact, providing practical guidance for optimizing subway ventilation system maintenance (e.g., tailored cleaning strategies for different locations) and ensuring passenger health. Full article

This study addresses the persistent challenges that students face in understanding redox reactions, particularly the link between symbolic and particulate representations in electrochemistry. The purpose was to explore whether physical modeling with clay could enhance the conceptual understanding of electron transfer and oxidation–reduction processes. Two groups of Swedish upper secondary students participated in instructional sessions: Group A used clay models to visualize electron movement, while Group B relied solely on symbolic notation. Data were collected through a written test and follow-up interviews. Results indicate that Group A outperformed Group B in tasks involving metal displacement and identifying the number of electrons transferred in a more complex reaction combining redox and acid–base processes. However, differences were minimal in synthesis reactions and fundamental conceptual questions. Both groups exhibited widespread alternative understandings, although Group A demonstrated fewer alternative understandings and greater accuracy in applying the concept of charge. The findings suggest that clay modeling can support the visualization of electron transfer and reduce alternative understandings but does not independently foster deeper conceptual understanding. The effective integration of modeling with explicit instruction on particulate-level reasoning and scientific terminology is essential in bridging representational gaps in electrochemistry. Full article

Air pollution remains a major global public health concern, with fine particulate matter (PM_2.5) recognized as an important environmental risk factor for lung cancer. This ecological study assessed lung cancer mortality attributable to long-term PM_2.5 exposure in the 26 Brazilian state capitals and the Federal District (Brasília) from 2014 to 2023. Annual mean PM_2.5 concentrations were estimated using reanalysis-based PM_2.5 concentration estimates and atmospheric reanalysis data, ensuring consistent spatial and temporal coverage. Mortality data were obtained from the Brazilian Mortality Information System (SIM/DATASUS). Health impacts attributable to PM_2.5 exposure were estimated using the World Health Organization’s AirQ+ model, based on exposure–response functions from the Global Burden of Disease framework. During the study period, 97.41% of annual PM_2.5 means exceeded the WHO Air Quality Guideline of 5 µg/m³, and 28.52% surpassed the current Brazilian regulatory limit. Higher concentrations were observed mainly in capitals from the North and Southeast regions, reflecting the influence of biomass burning, urbanization, and regional atmospheric processes. Approximately 13.56% of lung cancer deaths in Brazilian capitals were attributable to PM_2.5 exposure, with the highest absolute numbers concentrated in the Southeast region. These findings demonstrate a substantial and spatially heterogeneous lung cancer burden associated with urban air pollution in Brazil and highlight the need for strengthened air quality management and targeted urban public health policies. Full article

Fine particulate matter (${\mathrm{P}\mathrm{M}}_{2.5}$) is a hazardous air pollutant that poses serious risks to human health. Long-term exposure to high concentrations of ${\mathrm{P}\mathrm{M}}_{2.5}$ increases the likelihood of developing cardiovascular and respiratory diseases. Therefore, accurately monitoring ${\mathrm{P}\mathrm{M}}_{2.5}$ concentrations are crucial for effective air quality management. However, due to the limited number and uneven distribution of monitoring stations, traditional monitoring methods fail to provide comprehensive data. With advancements in imaging technology and data processing, researchers have focused on estimating ${\mathrm{P}\mathrm{M}}_{2.5}$ concentrations using image-based approaches. We constructed the ${\mathrm{P}\mathrm{M}}_{2.5}$-sensitive pixel (PSP) approach. In addition to the original four image features—Sobel, Dark Channel Prior (DCP), entropy, and contrast—we identified a new image feature and integrate three meteorological variables, relative humidity, temperature, and wind speed, to enhance the estimation of ${\mathrm{P}\mathrm{M}}_{2.5}$ concentrations. Full article

Air pollution is a major environmental health hazard. This study evaluates the health risks of air pollution exposure in the megacity Karachi, Pakistan, using the cigarette-equivalent technique developed previously for translating air pollution exposure into passive cigarette equivalents. Sampling of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and black carbon (BC) was performed at various fixed locations throughout the four seasons of the year. We evaluated the health risks of pollutants exposure using four different health endpoints including low birth weight (<2500 g at term after 37 weeks of gestation), decreased lung function (Forced Expiratory Volume in 1 s), cardiovascular mortality, and lung cancer in residents of Karachi. The average risks of low birth weight from PM_2.5, NO₂, and BC were 37.2, 14.8, and 1.01, respectively, (expressed as the equivalent number of passively smoked cigarettes, PSCs) while the average risks of decreased lung function were 93.9, 38.8, and 2.87. Risks of cardiovascular mortality were 51.9, 14.3, and 2.79, and those of lung cancer were 31.3, 6.47, and 1.32, respectively. The remarkably high risks are attributed to high concentrations of air pollutants. These results suggests that residents of Karachi may experience other adverse health effects beyond those typically attributed to air pollution. These PSC equivalent risks indicate a substantial potential health burden in Karachi and support the need for emission reduction efforts targeting traffic, industrial activity, and open burning. PM_2.5 and BC were measured in 2008–2011 and NO2 in 2008–2009, so the results should be interpreted as baseline risk estimates for that period rather than current (2025) concentrations. Full article