Search Results (2,656)

This study examines the seasonal and diurnal variations in ultrafine particles (PM_0.1) and their carbonaceous components (OC and EC), collected at the Institute of Technology of Cambodia in Phnom Penh. Sampling was conducted over 14 consecutive days in September 2024 (during the wet season) and February 2025 (during the dry season). The average mass concentration of PM_0.1 in February (8.5 μg/m³; range: 3.9–11.3 μg/m³) was approximately three times greater than that in September, driven by a corresponding increase in OC concentration. Conversely, average EC concentrations remained almost stable across both seasons, indicating consistent local emission sources. Total carbonaceous compounds (OC + EC) constitute approximately 50% of the PM_0.1 mass in both seasons. Primary organic carbon (POC) concentration increases almost four times in February compared to September. Secondary organic carbon (SOC) concentrations were significantly elevated during February daytime (1.4 ± 1.0 μg/m³), indicating active photochemical formation. Backward trajectory analysis and satellite hotspot data revealed that September air masses originated from maritime sources without significant local burning influences, while February pollution events were likely influenced by short-range transboundary transport from biomass-burning areas across the Cambodia–Vietnam border. Full article

This study investigates the long-term variability and evolution of particulate matter with an aerodynamic diameter of <2.5 µm (PM_2.5) sources in Vilnius, Lithuania, during the period 2013–2021. Source apportionment was performed using Positive Matrix Factorization (PMF) based on elemental composition data obtained through particle-induced X-ray emission (PIXE) analysis. The results revealed substantial year-to-year variability in the chemical profiles of the identified sources. Crustal/mineral dust was characterized by high contributions of lithogenic elements, including Si, Ca, Ti, and Fe, while soil dust exhibited elevated proportions of Al, Ca, and Fe. Traffic non-exhaust emissions were marked by elevated Cu, Zn, and Pb in 2013–2015, whereas exhaust emissions in 2019–2021 were characterized by sulfur-rich aerosols. Industrial and oil combustion sources showed enhanced contributions of Ni, V, and Cr, particularly in 2016, 2018, and 2020. Biomass/wood burning represented a major seasonal source, reaching peak intensity in 2018–2019 and characterized by elevated K and Zn contributions. A notable long-term trend was the increasing importance of soil-derived particles, as reflected by Al contributions rising to 91.2% by 2021. Overall, the major PM₂.₅ source categories remained relatively stable, while their chemical fingerprints and relative importance exhibited substantial temporal variability. Full article

Ambient fine particulate matter (${\mathrm{PM}}_{2.5}$) poses a significant health risk in Europe, where many cities are exposed to levels exceeding WHO and EU guidelines. Reducing population exposure, therefore, calls for targeted and effective mitigation strategies. To support the implementation of optimal ${\mathrm{PM}}_{2.5}$ reduction policies, high-resolution air quality modeling is necessary. In this context, source allocation studies aim to link the pollution at a specific location to different emitters, typically expressing the contribution of each in terms of concentration differences. An alternative approach is the use of relative potentials, defined as the share of ${\mathrm{PM}}_{2.5}$ concentration reduced at a given receptor resulting from the reduction in the emissions from a given source. To calculate relative potentials, Source-Receptor Relationships (SRRs) can be used to mimic Chemical Transport Models, saving significant computation time when simulating emission reduction scenarios. However, while the relative potential indicator is increasingly used to guide source allocation analyses, its robustness with respect to meteorological variability has not been systematically evaluated. Given that meteorology can be a major driver of ${\mathrm{PM}}_{2.5}$ inter-annual variability, assessing this robustness is a prerequisite for the optimal use of SRRs in air quality planning. To address this gap, we use the SRR model SHERPA, based on the Chemical Transport Model EMEP, to evaluate the robustness of relative potentials of 150 European cities across four contrasting meteorological years (2015, 2017, 2019, and 2021). The contributions of four spatial reduction scales, six emission sectors and five emission precursors are analyzed. Our results show that relative potentials vary little with meteorology for most cities, with low inter-annual ranges for most spatial scales, precursors and sectors. These trends are consistent with EMEP simulations. They establish the robustness of the relative potential indicator and of SRR-based source allocations with respect to meteorological variability, supporting their use in guiding targeted air quality policies in Europe. Full article

Heavy-duty transit vehicles (N1–N3) (heavy vehicles) can generate disproportionate environmental and economic impacts in urban transport corridors despite representing a relatively small share of total traffic volume. This study develops an integrated probabilistic framework for assessing the relationships between traffic-flow variability, CO₂e emissions, particulate-matter-derived climate impacts, and external pollution costs associated with transit transport. The methodology combines traffic-flow modeling, emission estimation, PM-to-CO₂e transformation, probabilistic analysis, Monte Carlo simulation, sensitivity analysis, and scenario-based intervention assessment. Separate analyses were conducted for M1 passenger vehicles and heavy vehicles to evaluate differences in emission behavior, uncertainty, and economic impacts. The results indicate substantial structural differences between light-duty and heavy-vehicle regimes. Passenger-car traffic exhibited relatively stable emission distributions, whereas heavy vehicles demonstrated significantly greater variability, uncertainty, and emission intensity. Sensitivity analysis identified heavy-vehicle flow as the dominant factor influencing overall system emissions and pollution costs. Scenario analysis indicated that restrictions targeting heavy-vehicle traffic have the potential to generate considerably larger environmental benefits than generalized traffic-reduction measures. Probabilistic assessment further revealed that heavy vehicles contribute disproportionately to high-emission risk regimes and uncertainty propagation within the system. The proposed framework provides an integrated approach for evaluating climate impacts, uncertainty and economic externalities of transit transport. The results highlight the importance of heavy-vehicle management in reducing emissions and pollution costs while supporting risk-informed transport policy development. Full article

Fuel poverty is shaped by interacting social, environmental and housing conditions, yet these links remain underexplored at city scale. The analysis is framed as an ecological, cross-sectional assessment of spatial associations rather than as a causal proof of a closed feedback mechanism. This study examines the relationship between fuel poverty, deprivation, particulate air pollution and housing typology across 54 wards in Liverpool, UK. Ward-level fuel poverty and Index of Multiple Deprivation (IMD) data were integrated with 2023–2024 annual mean particulate matter (PM_2.5 and PM₁₀) from 58 low-cost air-quality sensors and classified housing types. Regression models were used to compare individual, additive and interaction effects. Fuel poverty ranged from 12.4% to 25.29%, while PM_2.5 and PM₁₀ frequently exceeded World Health Organization guideline values. IMD was the strongest individual predictor of fuel poverty (R² = 0.281, $p<0.001$). The preferred additive model including IMD, PM_2.5, PM₁₀ and housing type explained 43.5% of the variance, with Victorian Terraces emerging as a significant risk factor. Although interaction models suggested pollution-deprivation coupling, model selection and uncertainty diagnostics favoured the simpler additive specification. The findings support targeted retrofit, fuel-poverty and emissions-control policies in deprived urban neighbourhoods where inefficient housing and environmental stressors compound energy insecurity and where local action can contribute to more equitable urban sustainability. Full article

Ozone (O₃) pollution is highly sensitive to meteorological variability and regional transport, particularly in coastal southeastern China. During April–May 2025, Xiamen experienced an atypical, persistent springtime O₃ episode substantially exceeding the 2014–2024 baseline. Using surface observations and ERA5 reanalysis data, this study investigates the meteorological drivers and formation mechanisms. At Hongwen station, the MDA8 O₃ > 160 μg m⁻³ exceedance frequency reached 11.5% (historical average: 0.1%). This anomaly was closely linked to an anomalous Western Pacific Subtropical High (WPSH) configuration, characterized by northward displacement and accompanying westward extension. Compared to historical high-pollution conditions, surface temperature and downward solar radiation increased by 2.32 °C and 51 W m⁻², while wind speed and planetary boundary layer height decreased by 15.3% and 24.2%, favoring O₃ production and precursor accumulation. Two distinct pollution periods were identified. Period 1 (29 April–1 May) featured local photochemical enhancement under stagnant conditions; regional mean NO₂ increased by 31 μg m⁻³ before the peak, indicating substantial precursor accumulation. Simultaneously, the mean nighttime O₃ concentration at the Huli site during Period 1 was 50.5 μg m⁻³ (43% lower than that at Hongwen) due to enhanced NO titration from port emissions. Period 2 (12–14 May) involved regional transport, where persistent 850-hPa southwesterly flow facilitated pollutant transport along the coastal corridor, increasing O₃ and PM_2.5 by 40 μg m⁻³ and 38 μg m⁻³. Thus, extreme springtime O₃ over southeastern coastal China resulted from anomalous large-scale circulation, regional transport, and local photochemical processes. Full article

This study presents a comparative life cycle assessment (LCA) of a conventional wet flue gas desulfurization (FGD) process and two carbonate-melt-based FGD configurations (CMFGD-H and CMFGD-T), based on a functional unit of 1 kg SO₂ removed. Process-level life cycle inventory (LCI) data were generated using process simulation to ensure consistency and comparability across all systems. The results indicate that both CMFGD configurations significantly reduce environmental impacts in terms of global warming potential (GWP), fine particulate matter formation (PM), and terrestrial acidification (TA) compared to the conventional FGD process. Specifically, GWP decreased from 177.75 kg CO₂ eq to 37.47 and 35.68 kg CO₂ eq for CMFGD-H and CMFGD-T, respectively. Similar reductions were observed for PM and TA, primarily due to the elimination of limestone consumption, the absence of gypsum waste generation, and reduced direct process emissions. Hotspot analysis revealed that direct CO₂ emissions dominate GWP across all configurations, whereas PM and TA are influenced by both direct emissions and upstream energy supply. In the CMFGD systems, environmental burdens shift from direct emissions toward upstream processes, particularly electricity and hydrogen production, highlighting the importance of energy system characteristics. However, a clear trade-off was identified in fossil resource scarcity (FRC), which increased significantly for CMFGD configurations (1.858–1.976 kg oil eq) compared to the conventional process (0.128 kg oil eq). This increase is primarily attributed to greater dependence on upstream energy supply chains, including fossil-based electricity, fuel, and hydrogen production. Sensitivity analysis further indicates that FRC is configuration-dependent, with hydrogen consumption dominating in CMFGD-H and CO utilization playing a more significant role in CMFGD-T. Nevertheless, even with reductions in these key parameters, FRC remains substantially higher than that of the conventional process, indicating that this impact is fundamentally governed by upstream energy dependency rather than individual process variables. The results demonstrate that CMFGD technologies offer substantial environmental benefits in terms of emission-related impacts but may increase resource depletion. These findings highlight that achieving sustainable CMFGD systems requires an integrated approach that combines process optimization with low-carbon and resource-efficient energy supply. Full article

Landfill tire fires are complex environmental disasters generating toxic pollutants with severe health risks. This study quantified emission dynamics and toxicological consequences of a large-scale tire fire in an urban ecosystem. A comprehensive source-to-receptor approach was applied, integrating Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric dispersion modeling with comparison against air quality monitoring data. Soil samples collected from the fireground and surrounding urban allotment gardens were analyzed for tire-specific tracers (Zn) and 16 priority polycyclic aromatic hydrocarbons (PAHs). Human health risks were assessed using Incremental Lifetime Cancer Risk (ILCR), Toxic Equivalency Quotient (TEQ), and Mutagenic Equivalency Quotient (MEQ) metrics. Fire emissions were dominated by particulate matter (PM₁₀: 1.34 t) and PAHs (17.7 kg). Soil at the fire site showed severe contamination (Σ PAHs: 148.9 mg/kg), with benzo[a]pyrene as the primary carcinogen. The cumulative ILCR for children reached 9.7 × 10⁻⁴, exceeding the commonly used upper regulatory benchmark of 10⁻⁴. Dermal contact was identified as the dominant exposure pathway for pyrogenic PAHs. Elevated risk levels persisted at distal residential sites (ILCR: 10⁻⁵–10⁻⁴), indicating long-term environmental contamination Ecological risk quotients (RQ) exceeded unity for PAHs across all fire-impacted locations and for Zn and Cu in the immediate vicinity of the fire scene. These findings demonstrate that acute tire fire events can evolve into persistent terrestrial health hazards, highlighting the critical role of dermal exposure in PAH uptake and the need for long-term environmental monitoring and adaptive land-use management strategies to mitigate chronic health risks in urban populations. Full article

The environmental effects of green finance policies involve complex systemic interactions across multiple levels, yet existing studies often adopt fragmented analytical approaches. Drawing on the multi-level perspective (MLP), this study conceptualizes the environmental impacts of Green Finance Reform and Innovation Pilot Zones (GFRIPZs) as a process of systemic green transformation involving interactions among landscape, regime, and niche levels. Using panel data of 287 prefecture-level and above cities in China from 2012 to 2022, this study applies a staggered difference-in-differences (DID) model to evaluate the environmental impacts of GFRIPZs. The results show that GFRIPZs significantly reduce both PM_2.5 concentrations and CO₂ emissions. Mechanism analyses based on multiple mediation models and GSEM reveal pollutant-specific differences in underlying channels. Green technological innovation (GTI) constitutes one observable pathway for PM_2.5, whereas the policy effect is more closely associated with energy structure adjustment for CO₂. Heterogeneity analysis further shows that PM_2.5 mitigation is stronger in colder cities, while CO₂ reduction is more pronounced in developed cities. These findings reveal pollutant-specific mechanisms of green finance and offer policy implications for developing countries seeking to promote systemic green transformation. Full article

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

Non-exhaust emissions (NEEs), particularly brake wear particles (BWPs), have become a dominant source of traffic-related particulate matter (PM), accounting for approximately 77% of PM10 and 60% of PM2.5 emissions. Accurate quantification of these emissions is essential under increasingly stringent regulations such as Euro 7. However, measurement reliability is strongly influenced by particle transport and sampling losses. This review provides a state-of-the-art analysis of laboratory-scale methodologies for investigating BWP emissions, focusing on pin-on-disc (PoD) tribometers and inertia dynamometer systems. Particular attention is given to chamber design, airflow management, sampling configurations, and the mechanisms governing particle transport efficiency. The literature indicates that PoD systems are often affected by complex and non-uniform flow fields, leading to incomplete particle capture and reduced representativeness, whereas inertia dynamometers, especially when coupled with constant volume sampling (CVS), provide more controlled and reproducible conditions. Key loss mechanisms, including inertial deposition, diffusion, gravitational settling, and non-isokinetic sampling effects, are major contributors to uncertainty. The reviewed studies highlight that aerodynamic limitations in PoD systems, particularly box-shaped chambers, promote flow recirculation and particle losses. Advanced optimization approaches that combine artificial neural networks (ANNs) with computational fluid dynamics (CFD) simulations show strong potential to improve system design and measurement reliability. Full article

Fine particulate matter with a diameter ≤2.5 μm (PM_2.5) pollution poses a global public health crisis, demonstrating significant threats to human health. This study focused on the strategically important Chengdu-Chongqing Economic Circle in western China, systematically comparing the toxic effects of urban and rural PM_2.5 across five levels. PMF and regression analysis were used to identify source contributions, dual-omics to pinpoint key molecules, and epidemiological data with a GAM model to assess health risks. Findings demonstrate that rural PM_2.5 possesses greater biotoxicity than its urban counterpart. Cytotoxicity in urban and rural PM_2.5 originated from road dust/vehicle emissions and biomass burning, respectively. Subsequently, integrated omics and molecular biology analyses identify kinesin family member 20A (KIF20A) as a shared key target, which mediates toxicity induced by both urban and rural PM_2.5. Finally, epidemiological analysis reveals that females and ≥65 years old exhibit relatively high sensitivity to urban PM_2.5 exposure trends, with rhinitis showing a comparatively higher impact among various related diseases. The novelty of this work lies in its pioneering application of a multi-tiered investigative approach. This approach spans “environmental samples-cellular mechanisms-population health” within the Chengdu-Chongqing economic circle context, systematically elucidating common and distinct respiratory health risk of urban and rural PM_2.5. This work offers a vital scientific foundation for advancing region-specific, precise air pollution prevention and control measures. Full article

The environmental impacts of mine dust in mining operations can be mitigated through improved prediction of its spatial distribution using dispersion models, particularly Gaussian-based air quality models. However, Gaussian-based models often predict concentrations that differ substantially from observed mine dust behavior, because dust properties and transport mechanisms vary markedly with particle size. In this study, particle-size-related mechanisms for dust dispersion behaviors were classified as dry/wet deposition, turbulent diffusivity, erosion, hygroscopicity, or agglomeration, and their effects on dust dispersion behaviors and effective simulation methods were reviewed. Currently, the most clearly established particle size influence is on deposition, especially for coarse dust emitted from mechanical mining processes. Other mechanisms, including erosion, hygroscopicity, and agglomeration, are more relevant to finer dust below 2.5 µm or in the submicron range. This study proposes that wind erosion, mainly saltation flux, can also be integrated into Gaussian dispersion models as near-ground boundary flux terms. Hygroscopic and agglomeration effects can be assessed using relative humidity and simplified particle size redistribution assumptions near dust emission sources. In particular, incorporation of agglomeration mechanisms may begin with a simple bimodal assumption: the agglomeration of PM_2.5 into PM₁₀. This can be incorporated into a modified Gaussian deposition equation. Finally, the size dependence of the turbulent diffusivity coefficient is relatively insignificant, so the diffusivity values can be regarded as constants. These findings provide a mechanistic basis for improving mine dust prediction and environmental management in open-pit mines, haul roads, tailings areas, and stockpile environments. Full article

Air pollution is a major environmental determinant of premature mortality and population health burden. Liguria represents a vulnerable Mediterranean region due to intense urbanisation, port-related emissions, complex topography and an ageing population. This study quantified the mortality burden and Years of Life Lost (YLL) attributable to long-term exposure to PM_2.5, NO₂ and O₃ in Liguria (Italy), and estimated the potentially avoidable burden under WHO guideline scenarios. A Health Impact Assessment (HIA) was conducted using ARPAL air quality data and ISTAT mortality data for the population aged ≥30 years during 2022–2024. Relative risks were derived from the European ELAPSE project and WHO meta-analyses. Attributable mortality was estimated using a log-linear Health Impact Function, while YLL were calculated using regional life tables and normalised per 100,000 inhabitants. PM_2.5 was the main contributor to air pollution-related mortality, accounting for 1333 attributable deaths in 2022. Corresponding YLL ranged from 755 to 1012 per 100,000 inhabitants over the study period. NO₂ showed a relevant but secondary contribution, while O₃ effects were smaller and more uncertain. WHO guideline scenarios indicated a substantial potentially avoidable burden of deaths and YLL. These findings support targeted environmental and public health interventions in highly urbanised coastal regions. Full article

This study applies a Long Short-Term Memory (LSTM) model to predict key air pollutants, i.e., sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM_2.5), as well as the Air Quality Index (AQI) across South Africa using satellite-derived observations. The analysis focuses on comparing original pollutant fields with model-generated predictions for two consecutive days, highlighting both spatial patterns and predictive performance. Results reveal a persistent and intense pollution hotspot over the Mpumalanga Highveld, driven by coal-fired power generation and industrial activities. Elevated pollutant concentrations in this region translate into AQI levels ranging from Unhealthy to Very Unhealthy, while most other parts of the country remain within the Good category. Spatial comparison between original and predicted fields shows strong agreement, with only minor deviations in areas characterized by steep emission gradients and localized plumes. Quantitative evaluation using RMSE (0.020390) and MSE (0.000416) confirms the high accuracy of the predictive model, with error values remaining extremely low across all pollutants and AQI outputs. PM_2.5 exhibits the smallest errors (MSE = 4.230169 × 10⁻⁶), while slightly higher values for SO₂ (MSE = 2.628 × 10⁻⁴) and NO₂ (MSE = 1.39541 × 10⁻⁴) reflect the difficulty of capturing sharp spatial transitions associated with point-source emissions. Despite these localized discrepancies, the model demonstrates robust skill in replicating both pollutant magnitudes and AQI classifications. Overall, the findings indicate that machine-learning approaches offer a reliable, high-resolution tool for air-quality prediction in South Africa and have strong potential for supporting operational forecasting, exposure assessment, and environmental policy development. Full article