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(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretable summaries that are not limited to simple site or time segmentation. (2) Methods: We carried out a multi-site campaign across five traffic-affected micro-environments, where measurements covered several pollutants, gases, and meteorological variables. A machine learning framework was introduced to learn interpretable operational regimes as recurring multivariate states using clustering with stability checks, and then we evaluated their added explanatory value and cross-site transfer using a strict site hold-out design to avoid information leakage. (3) Results: Five regimes were identified, representing combinations of emission intensity and ventilation strength. Incorporating regime information increased the explanatory power of simple NO₂ models and allowed the imputation of missing H₂S day using regime-aware random forest with an $R^2$ near 0.97. Regime labels remained identifiable using reduced sensor sets, while cross-site forecasting transferred well for NO₂ but was limited for PM, indicating stronger local effects for particles. (4) Conclusions: Operational-regime learning can transform short multivariate campaigns into practical and interpretable summaries of urban air pollution, while supporting data recovery and cautious model transfer. Full article

Public health impacts of non-optimal temperatures and air pollution have received insufficient attention in Southeast Europe, one of the most air-polluted regions in Europe, simultaneously pressured by climate change. This study employed a multimodal approach to characterize the microclimate and air quality and conduct a health impact assessment in the three biggest cities in Bulgaria. Simulation of atmospheric thermo-hydrodynamics and assessment of urban microclimate relied on the Weather Research and Forecasting model. Concentrations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) were calculated with a land-use regression model. Ischemic heart disease (IHD) hospital admissions were linked to daily measurements at background air quality stations. The results showed declining trends in PM_2.5 but persistent levels of NO₂, especially in Sofia and Plovdiv. Distributed lag nonlinear models revealed that, in Sofia and Plovdiv, PM_2.5 was associated with IHD hospitalizations, with a fifth of cases in Sofia attributable to PM_2.5. For NO₂, an increased risk was observed only in Sofia. In Sofia, the risk of IHD was increased at cold temperatures, while both high and low temperatures were associated with IHD in Plovdiv and Varna. Short-term effects were observed in response to heat, while the effects of cold weather took up to several weeks to become apparent. These findings highlight the complexity of exposure–health interactions and emphasize the need for integrated policies addressing traffic emissions, urban design, and disease burden. Full article

The composition of air pollutants in industrial complexes differs from that of general urban areas, often containing more hazardous substances that pose significant health risks to both workers and residents nearby. In this study, PM_2.5 and its 29 chemical components (eight ions, two carbon species, and 19 trace elements) were measured and analyzed at five monitoring sites adjacent to the Yeosu and Gwangyang industrial complexes from August 2020 to December 2024. Chemical characterization and source identification were conducted. The average PM_2.5 concentration was 18.63 ± 9.71 μg/m³, with notably higher levels observed during winter and spring. A low correlation (R = 0.56) between elemental carbon (EC) and organic carbon (OC) suggests a dominance of secondary aerosols. The charge balance analysis of [NH₄⁺] with [SO₄²⁻], [NO₃⁻], and [Cl⁻] showed slopes below the 1:1 line, indicating that NH₄⁺ is capable of neutralizing these anions. Positive matrix factorization (PMF) identified eight contributing sources—biomass burning (10.4%), sea salt (11.8%), suspended particles (7.1%), industrial sources (4.6%), Asian dust (5.2%), steel industry (21.8%), secondary nitrate (16.4%), and secondary sulfate (22.7%). These findings provide valuable insights for the development of targeted mitigation strategies and the establishment of effective emission control policies in industrial regions. Full article

The aim of the presented article is the comparison of external monetary environmental impacts generated by railway and road transport in Slovakia region. The Kralovany–Trstena regional line located in the northern Slovakia was selected. The monetary impacts of environmental pollution from transport on this line were analysed. Various pollutants were selected for the assessment from exhaust gases, such as fine particulate matter PM_2.5, nitrogen oxides (NO_x), sulphur dioxide (SO₂), non-methane volatile organic compounds (NMVOC), and ammonia (NH₃). The Cost–Benefit Analysis methodology was applied for the calculation of monetary impacts. The results point out that the usage of diesel multiple units on the given line has a significant impact on the monetary impacts of pollutant emissions. Full article

Carbon dioxide (CO₂) emissions are a major contributor to climate change, requiring sustainable carbon capture and utilization (CCU) strategies. This study employed density functional theory (DFT) to assess a choline chloride–lactic acid deep eutectic solvent (CHL–LAC DES) as a dual system for CO₂ capture and gingerol extraction. Using the wB97X-D functional theory for energy calculation with PM3-optimized geometries, the DES exhibited stronger CO₂ binding (–0.86 eV) than monoethanolamine (–0.234 eV) and a higher affinity for 6-gingerol (–1.87 eV). These results suggest that CHL–LAC DES can simultaneously capture CO₂ and extract bioactive compounds, advancing green pharmaceutical and integrated CCU applications. Full article

PM_2.5 pollution has become one of the major environmental issues in Shandong Province in recent years. High concentrations of PM_2.5 not only reduce atmospheric visibility but also induce respiratory and cardiovascular diseases, and significantly increase health risks. Source apportionment of PM_2.5 is important for policy makers to determine control strategies. This study analyzed regional and sectoral PM_2.5 sources across 17 Shandong cities during the 2017–2018 winter heating season, which is selected because it is representative of severe air pollution with an average PM_2.5 of 65.75 μg/m³ and hourly peak exceeding 250 μg/m³. This air pollution episode aligned with key control policies, where seven major cities implemented steel capacity reduction and coal-to-gas/electric heating, as a baseline for evaluating emission reduction effectiveness. The particulate matter source apportionment technology in the Comprehensive Air Quality Model with extensions (CAMx) was applied to simulate the source contributions to PM_2.5 in 17 cities from different regions and sectors including industry, residence, transportation, and coal-burning power plants. The meteorological fields required for the CAMx model were generated using the Weather Research and Forecasting (WRF) model. The results showed that all cities besides Dezhou city in Shandong Province contributed PM_2.5 locally, varying from 39% to 53%. The emissions from Hebei province have a large impact on the PM_2.5 concentrations in Shandong Province. The non-local industrial and residential sources in Shandong Province accounted for the prominent proportion of local PM_2.5 in all cities. The contribution of non-local industrial sources to PM_2.5 in Heze City was up to 56.99%. As for Zibo City, the largest contribution of PM_2.5 was from non-local residential sources, around 56%. Additionally, the local industrial and residential sources in Jinan and Rizhao cities had relatively more contributions to the local PM_2.5 concentrations compared to the other cities in Shandong Province. Finally, the emission reduction effects were evaluated by applying different reduction ratios of local industrial and transportation sources, with decreases in PM_2.5 concentrations ranging from 0.2 to 26 µg/m³ in each city. Full article

Cropland fires are an important source of air pollution emissions and have a significant impact on regional air quality and human health. Although straw-burning ban policies have been implemented to mitigate emissions, the dynamics of PM_2.5 emissions from cropland fires under such stringent regulations are still not fully understood. This study utilizes PM_2.5 emission data from the Global Fire Assimilation System (GFAS), land-cover data from CLCD, and PM_2.5 concentration data from ChinaHighAirPollutants (CHAP) to examine the dynamic evolution of PM_2.5 emissions from cropland fires under straw-burning ban policies across China and to assess their environmental impacts. The results show that the 2013 Air Pollution Prevention and Control Action Plan initiated the development of provincial straw-burning ban policies. These policies resulted in a drastic reduction in PM_2.5 emissions from cropland fires in North China (NC), with a 65% decrease in 2022 compared to the 2012 peak. In contrast, a notable lagged effect was observed in Northeast China (NEC), where the increasing trend of PM_2.5 emissions was not reversed until 2017. By 2022, emissions in this region had declined by 53% and 45% compared to the 2015 peak and 2017 sub-peak, respectively. Moreover, significant regional differences were found in the environmental impacts of PM_2.5 emissions from cropland fires, with strong effects during summer in NC and during spring and autumn in NEC. This study provides empirical support for understanding the environmental impacts of cropland fires in key regions of China and offers critical insights to inform and refine related pollution control policies. Full article

This study evaluates the effectiveness of air pollution control measures in Xi’an, China, by investigating long-term changes in the concentrations, optical properties, and sources of black carbon (BC) and brown carbon (BrC). Wintertime observations of PM_2.5 carbonaceous aerosols were conducted over multiple years using a continuous Aethalometer. The data were analyzed using advanced aethalometer models, potential source contribution function (PSCF) analysis, and generalized additive models (GAMs) to deconstruct emission sources and formation pathways. Our results revealed a significant decrease in the mass concentration and light absorption coefficient of BC (b_abs-BC) between the earlier and later study periods, indicating successful emission reductions. In contrast, the light absorption from BrC (b_abs-BrC) remained relatively stable, suggesting persistent and distinct emission sources. Source apportionment analysis demonstrated a temporal shift in dominant regional influences, from biomass burning in the initial years to coal combustion in later years. In addition, GAMs showed that the primary driver for liquid fuel-derived BC transitioned from gasoline to diesel vehicle emissions. For solid fuels, residential coal combustion consistently contributed over 50% of BC, highlighting that improvements in coal combustion technology were effective in reducing BC emissions. Furthermore, a substantial fraction of BrC was increased, with nocturnal peaks associated with high relative humidity, emphasizing the aqueous-phase formation influences. Collectively, these findings demonstrated that although certain control strategies successfully mitigated BC, the persistent challenge of BrC pollution necessitates targeted measures addressing secondary formation and primary fossil fuel sources. Full article

Microalgal amendments can improve soil structure by regulating extracellular polymeric substances (EPSs). However, the mechanisms underlying this process in red soils (characterized by high clay content and susceptibility to acidification) under different farming practices remain unclear. This study examined how Chlorella vulgaris (C. vulgaris) amendment influences EPS composition to enhance soil aggregate stability under arable land and rice paddy farming. A five-month pot experiment using a completely randomized design was conducted to investigate the effects of Chlorella vulgaris amendment on soils cultivated with Pennisetum × sinese and rice, two economically important crops commonly grown in South China. At the end of the experiment, Chlorella vulgaris amendment substantially increased both the mean weight diameter (MWD) and geometric mean diameter (GMD) of soil aggregates under both farming systems. Excitation–emission matrix (EEM) fluorescence spectroscopy revealed distinct changes in soil EPS components between the two farming types. Under arable land farming, humic-like and protein-like EPSs were dominant in Chlorella vulgaris-amended treatments, with fluorescence intensities more than doubling compared to the control. Conversely, under rice paddy farming, soil fulvic acid was the main component and showed a moderate increase. Partial least squares path modeling (PLS-PM) demonstrated that protein-like and humic-like EPSs had the strongest direct effects on aggregate stability in arable land red soil, while fulvic acid was the key factor in rice paddy red soil. The present study demonstrates that Chlorella vulgaris amendment improves aggregate stability in red soils through farming-specific, EPS-mediated pathways, providing a quantitative framework for researchers and land managers seeking to apply microalgal amendments for red soil enhancement and sustainable land management. Full article

Following the implementation of the Shanghai Clean Air Act, this study investigates the evolution of air pollution in central Shanghai (Putuo District) by analyzing continuous monitoring data (2016–2020) and chemical speciation of particulate matter (2017–2018). The results confirm a transition toward a “low exceedance rate and low background concentration” regime. However, short-term exceedance episodes persist, generally occurring in winter and spring, with significantly amplified diurnal variations on exceedance days. Distinct patterns emerged between PM fractions: PM₁₀ exceedances were characterized by a single morning peak linked to traffic-induced coarse particles, while PM_2.5 exceedances showed synchronized diurnal peaks with NO₂, suggesting a stronger contribution from vehicle exhaust. Source apportionment revealed that mineral components (21.61%) and organic matter (OM, 21.02%) dominated in PM₁₀, implicating construction and road dust. In contrast, PM_2.5 was primarily composed of OM (26.73%) and secondary inorganic ions (dominated by nitrate), highlighting the greater importance of secondary formation. The findings underscore that sustained PM_2.5 mitigation requires targeted control of gasoline vehicle emissions and gaseous precursors. Full article

Northern Thailand experiences recurrent seasonal haze driven by biomass burning (BB), which results in hazardous PM2.5 exposure and elevated environmental health risks. To address the need for timely and spatially resolved emission information, this study developed and evaluated an operational near-real-time (NRT) biomass-burning PM2.5 emission estimation system using the Fire INventory from NCAR version 2.5 (FINNv2.5). The objectives of this study are threefold: (1) to construct a high-resolution (≤1 km) NRT biomass-burning PM2.5 emission inventory for Northern Thailand; (2) to assess its temporal and spatial consistency with ground-based PM2.5 measurements and satellite fire observations; and (3) to examine its potential utility for informing environmental health risk management. The developed system captured short-lived, high-intensity burning episodes that defined the haze crisis, revealing a distinct peak period from late February to early April. Cumulative emissions from January to April 2024 exceeded 250,000 tons, dominated by Chiang Mai (25.8%) and Mae Hong Son (25.5%), which together contributed 51.3% of regional emissions. Strong correspondence with MODIS/VIIRS FRP (r = 0.79) confirmed the reliability of the NRT emission signal, while regression against observed PM2.5 concentrations indicated a substantial background burden (intercept = 40.41 μg m⁻³) and moderate explanatory power (R² = 0.448), reflecting additional meteorological and transboundary influences. Translating these relationships into operational metrics, an Emission Control Threshold of 1518 tons day⁻¹ was derived to guide targeted burn permitting and reduce population exposure during peak-risk periods. This NRT biomass-burning PM2.5 emission estimation framework offers timely emissions information that may support decision makers in environmental health risk management, including the development of early warnings, adaptive burn-permit strategies, and more coordinated responses across Northern Thailand. Full article

Air pollution from lignite combustion represents a major environmental and public health concern, particularly for cardiovascular disease. This study investigated the relationship between ambient air pollution and hospital admissions for Acute Coronary Syndromes (ACS) and Atrial Fibrillation (AF) in Western Macedonia, Greece—a region historically dominated by lignite mining and power generation. Air quality data for PM₁₀, SO₂, and NO_x from 2011–2014 and 2021 were analyzed alongside hospital admission records from four regional hospitals (Kozani, Ptolemaida, Florina, Grevena). Spatial analyses revealed significantly higher pollutant concentrations and cardiovascular admissions in high-exposure areas near power plants compared with the control area. Temporal analyses demonstrated a pronounced decline in pollutant levels between 2014 and 2021, coinciding with lignite phase-out and accompanied by a marked reduction in ACS and AF hospitalizations, particularly in the high-exposure areas of Ptolemaida and Florina. Correlation analyses indicated modest but significant positive associations between monthly pollutant concentrations and cardiovascular admissions. These findings provide real-world evidence that reductions in air pollution following lignite decommissioning were associated with improved cardiovascular outcomes. The study underscores the medical importance of air quality improvement and highlights emission reduction as a critical strategy for cardiovascular disease prevention in transitioning energy regions. Full article

The study of energy needs in rural areas continues to be an active field of research. Although numerous gaps hinder the achievement of a sustainable energy transition in these areas, it is necessary to develop comprehensive strategies that integrate local participation with the implementation of efficient and appropriate energy technologies. This research analyzes local energy needs using a community participatory approach and considers four main stages, including a participatory diagnosis at the community level to identify energy needs, defining priority energy needs from the community’s viewpoint, estimating a baseline of the identified needs, their economic costs, and environmental impacts, constructing a scenario with a 20-year projection, and the benefits of implementing more efficient technologies. The results show that 98.9% of energy is destined for residential needs, 0.6% for community needs, and 0.5% for productive needs, and the economic expenditure follows the same hierarchy, while total emissions are estimated annually at just over 30,000 tCO_2e and 3 tPM_2.5. With the proposed scenario, at the end of year 20, a reduction in consumption of just over 200 TJ is estimated, together with present value savings of USD 490,000, and a decrease in emissions of approximately 27,000 tCO_2e and 2.7 tPM_2.5. This proposal is expected to contribute to encouraging research with broad community participation and to the formulation of strategies that enable a sustainable energy transition in rural contexts. Full article

Accurate quantification of aircraft emissions and their uncertainties is essential for well-informed policy-making, air quality management, and the development of sustainable airport strategies. This study addresses uncertainties in aircraft emission estimates implemented for local air pollutants with hourly resolution at six European airports. Publicly available flight-tracking data were used to determine aircraft movements and types, but they typically lack detailed information on aircraft engine models, thus contributing to uncertainties in emission factors. Times-in-mode for take-off, climb-out, and approach modes followed International Civil Aviation Organization (ICAO) recommendations, while taxi times, known to vary between airports, were modeled using statistical distributions derived from Eurocontrol, and the contribution of taxi time to overall uncertainty in emission estimates was investigated. Monte Carlo simulation combined with Sobol sensitivity analysis identified the relative contribution of each uncertainty source. On average, the results indicate an uncertainty of 23% for CO, 34% for HC, 7% for NO_x, and 21% for PM across the airports analyzed. Overall, the proposed methodology introduces a novel framework utilizing publicly available, hourly resolved flight-tracking data with robust uncertainty analysis to estimate airport-level emissions with enhanced reliability, providing crucial information for local air quality assessment and policy development. Full article

This study quantifies energy consumption and tank-to-wheel (TTW) emissions of urban buses under varying traffic conditions and passenger loads in Osnabrück, Germany, to support emission-aware route assessment in sustainable mobility applications. Exemplary bus trajectories were modeled on a representative 6.17 km route of line M5 (18 m articulated bus; diesel and battery-electric) within a 22.31 km² traffic net using the Simulation of Urban MObility (SUMO) software, and were calibrated with traffic sensor data. To assess the influence of trajectories in different traffic situations, three different 90 min scenarios were compared (morning peak, noon, night). Trajectory-based energy consumption and greenhouse gas emissions were compared by using the SUMO-implemented emission models HBEFA and PHEMlight, as well as data from the literature. Both diesel and electric buses showed variations in energy consumption depending on the traffic conditions, with generally lower energy consumption for electric propulsion. Temporal differences in the TTW emissions of the diesel bus were modest, with slightly higher morning values, while spatial analysis showed PM peaks in pedestrian zones, NO_x peaks during acceleration phases, and CO₂ increases after stops and in low-speed areas. The results provide spatially resolved TTW factors for integration into routing applications, excluding upstream and non-exhaust processes in line with the defined system boundary. Full article