Search Results (519)

Civilisational progress contributes to an increase in the number of vehicles on the road, thereby intensifying air pollutant emissions and accelerating the degradation of the natural environment. Effective protection of urban areas against air pollution enhances safeguarding against numerous allergies and diseases resulting from unplanned and unintended absorption of harmful pollutants into the human body. Sustainable urban planning requires the collaboration of multiple scientific disciplines. In this context, measurement becomes crucial, as it reveals the spatial scale of the problem and identifies existing disparities. This study uses an integrated approach of standard measurement methods and statistical and geostatistical data analysis, identifying PM₁ fractions that are not included in EU air quality monitoring. The hypothesis explores how surface-based results correspond to point-based results from national air quality monitoring. The presented implications demonstrate similarities and differences between the studied measurement methods and the spatial distributions of PM₁₀, PM_2.5, and PM₁ dust. Full article

Road dust resuspension is widely recognized as a major contributor to traffic-related particulate matter (PM) in urban environments. Nevertheless, reported emission factors exhibit substantial variability. These discrepancies stem not only from the intrinsic complexity of the resuspension process but also from limitations in measurement techniques, which often fail to adequately control or characterize the influencing parameters. As a result, the contribution of each parameter remains difficult to isolate, leading to inconsistencies across studies. This study presents an experimental protocol developed to quantify PM10 and PM2.5 emission factors associated with vehicle-induced road dust resuspension. Experiments were conducted on a dedicated test track seeded with alumina particles of controlled mass and size distribution to simulate road dust. A network of microsensors was strategically deployed at multiple upwind and downwind locations to continuously monitor particle concentration variations during vehicle passages. Emission factors were derived through time integration of the mass flow rate of resuspended dust measured by the sensor network. The estimated PM10 emission factor showed excellent agreement, within 2.5%, with predictions from a literature-based formulation, thereby validating the accuracy and external relevance of the proposed protocol. In contrast, comparisons with U.S. EPA formulas and other empirical equations revealed substantially larger discrepancies, particularly for PM2.5, highlighting the persistent limitations of current modeling approaches. Full article

Airborne dust emissions from bulk cargo handling in port terminals can degrade local air quality, but traditional dispersion models are often too slow or coarse to support rapid operational decisions. There is thus a pressing need for efficient tools that retain the spatial detail of CFD while enabling near-real-time scenario evaluation. In this work, we develop and test a hybrid framework that couples an RANS-based CFD model of dust dispersion with a neural network surrogate to rapidly predict exposure patterns for a bulk terminal under variable wind and operational conditions. The ML surrogate model, based on a decoder-style Multilayer Perceptron (MLP) architecture, processes two-dimensional slices of dispersion fields across particle diameter classes, enabling predictions in milliseconds with an acceleration factor of approximately $8\times {10}^6$ over traditional CFD while preserving high fidelity, as validated by performance metrics such as the F₁ score and precision values exceeding $0.8$ and $0.76$, respectively. This approach not only addresses computational inefficiencies but also lays the groundwork for real-time air-quality monitoring and sustainable urban planning, potentially integrating with digital twins fed by live weather data. Full article

In this study, the authors wish to introduce an unsupervised procedure designed for real-time generation of maps depicting advected aerosols, specifically focusing on desert dust and smoke originating from biomass combustion. This innovative approach leverages the high-resolution capabilities provided by Sentinel-2 imagery, operating at a 10 m scale, which is particularly advantageous for urban settings. Concurrently, it takes advantage of the near-daily revisit frequency afforded by Sentinel-3. The methodology involves generating aerosol maps at a 10 m resolution using bands 2, 3, 4, and 5 of Sentinel-2, available in L1C and L2A formats, conducted every five days, contingent upon the absence of cloud cover. Subsequently, this map is enhanced every two days through spatial modulation, utilizing a similar map derived from the visible and near-infrared observations (VNIR) captured by the OLCI instrument aboard Sentinel-3, which is accessible at a 300 m scale. Data from the two satellites undergo independent processing, with integration at the feature level. This process combines Sentinel-3 and Sentinel-2 maps to update aerosol concentrations in each 300 m × 300 m grid every two days or more frequently. For the dates when Sentinel-2 data is unavailable, the spatial texture or the aerosol distribution within these grid cells is extrapolated. This spatial index represents an advancement over prior studies that focused on differentiating between dust and smoke based on their scattering and absorption characteristics. The entire process is rigorously validated by comparing it with point measurements of fine- and coarse-mode Aerosol Optical Depth (AOD) obtained from AERONET stations situated at the test sites, ensuring the reliability and accuracy of the generated maps. Full article

In parallel with achieving its 2040 Vision toward establishing smart cities, this study aims to pinpoint promising locations for future urban development in Oman, which reflect the unique physical attributes of the country, its renewable energy resources, and socio-economic conditions. To meet this goal at the national scale, the research relied on the following key factors: topography, diurnal temperature range, relative humidity, dust concentrations, wind speed, solar radiation, and access to electricity. These inputs were derived from remote sensing sources. A multi-layer spatial analysis was carried out within a Geographical Information System (GIS) environment to identify high-priority locations for future and sustainable urban growth. All parameters were assigned equal weights, particularly when applying a standard approach to produce a baseline suitability model at the national scale and to avoid subjective bias in the overall suitability assessment. Results showed that 2.1% of Oman’s land shows strong potential for sustainable urban development. Specifically, three locations stand out with the highest occurring along the southern section of the Arabian Sea between Al Jazir and Ad-Duqum. The other two locations occur at Salalah in the south and Sohar in the north. The promising locations occur proximate to major harbors and can benefit from existing infrastructure, including airports, highways, educational and medical services. Suggested locations also align well with earlier relevant studies. This study demonstrates the capabilities of integrating remotely sensed data with geospatial analysis in urban planning and development. Results are expected to help policymakers and planners to prioritize national-scale urban development. Full article

In the context of dust pollution contributing more than 30% to PM_2.5 during urbanization, this study optimally designed a multi-component coupled dust suppressant based on the coupling mechanism of chemical dust suppressants, oriented towards environmental friendliness. The concentration range of the core component was determined through single-factor experiments: surfactant sodium dodecylbenzene sulfonate (SDBS) 0.5–1.0% (minimum surface tension 27.8 mN/m), coagulant sodium polyacrylate 0.1–0.2% (viscosity ≥ 42 mPa·s), and water-retaining agent triethanolamine 0.1–1.0% (3 h water retention > 90%). The L9 (3⁴) orthogonal test was used to optimize the formulation with water retention rate, crust hardness, and wind erosion rate as indicators, combined with range and variance analysis (α = 0.05). The results showed that sodium polyacrylate concentration had an extremely significant effect on water retention (contribution rate 98.6%), and an increase in its concentration significantly enhanced shell hardness (up to 51HA) and reduced wind erosion rate (down to 0.05%). The optimal ratio was 0.2% sodium polyacrylate, 1.0% sodium dodecylbenzene sulfonate, and 2.5% triethanolamine. At this time, the 24 h water retention rate reached 35.14%, and the wind erosion resistance was 16 times higher than that of the control group. The system builds a three-dimensional cross-linked structure through a hydrogen bond network to synergistically achieve enhanced dust wetting, particle coalescence, and long-lasting consolidation, providing theoretical support and practical solutions for green dust suppression technology. Full article

Methane (CH₄) is a potent greenhouse gas and a key target for near-term climate mitigation, yet major uncertainties remain in quantifying emissions from landfills, particularly in rapidly urbanizing regions of the Global South. Here, we present a systematic satellite-based assessment of CH₄ emissions from landfills and related sites across northern and central Brazil, based on plume detections from the Carbon Mapper public data portal. Using imaging spectroscopy data from the Earth Surface Mineral Dust Source Investigation (EMIT) onboard the International Space Station and the dedicated Tanager-1 satellite, we analyzed 40 plume detections across 16 sites in nine Brazilian states spanning the Amazon forest biome and the Cerrado transition region. An adaptive thresholding algorithm was applied to each detection to quantify plume strength (ppm·m³), areal extent, and recurrence across multiple overpasses. Our results reveal a strongly heavy-tailed distribution of emissions, with most sites exhibiting modest plume strengths in the 10⁶–10⁷ ppm·m³ range, while a small number of facilities dominated the upper tail. Two detections at Brasília (2.22 × 10⁸ and 2.14 × 10⁸ ppm·m³) and one at Marituba (1.66 × 10⁸ ppm·m³) were classified as super-emitters, exceeding all other sites by more than an order of magnitude. These facilities also demonstrated high persistence across overpasses, in contrast to smaller landfills such as Macapá and Boa Vista, where emissions were weaker (<10⁷ ppm·m³) and episodic. Regional contrasts were also evident: sites in the Cerrado transition zone, (e.g., Brasília, Campo Grande) generally showed stronger and more frequent emissions than those in the Amazon basin. These findings underscore the disproportionate role of a few persistent super-emitters in shaping the regional CH₄ budget. Targeted mitigation at these high-impact sites could yield rapid and cost-effective emission reductions, directly supporting Brazil’s commitments under the Paris Agreement and the Global CH₄ Pledge. More broadly, this study demonstrates the power of high-resolution satellite imaging spectroscopy for identifying, monitoring, and prioritizing CH₄ mitigation opportunities in the waste sector. Full article

This study utilizes backward trajectory cluster analysis, the Potential Source Contribution Function (PSCF), Concentration Weighted Trajectory (CWT), and a random forest model to investigate the pollution characteristics of PM_2.5 and PM₁₀ in the “Urumqi-Changji Hui Autonomous Prefecture-Shihezi-Wujiaqu (U-C-S)” urban agglomeration. Findings indicate that on an annual basis, higher PM_2.5 concentrations are observed in the central part of the “U-C-S” urban agglomeration, southern Wujiaqu, and the Shihezi area, whereas PM₁₀ concentrations are lower in the high-altitude regions of the Tianshan and Bogda Mountains. Seasonally, both PM_2.5 and PM₁₀ concentrations significantly increase during winter, with summer exhibiting the best air quality. On a monthly scale, Urumqi’s central urban area shows a marked rise in PM_2.5 concentrations during winter, attributed to coal heating and stable weather conditions. Weekly patterns reveal higher pollution levels on weekdays compared to weekends. Daily data show that PM_2.5 concentrations are notably higher in winter compared to other periods, while elevated PM₁₀ levels in spring are primarily due to dust storms. Cluster analysis indicates that seasonal airflow paths significantly influence particulate matter concentrations. PSCF and CWT analyses demonstrate that the most severe PM_2.5 pollution in winter is concentrated in the northern part of the Bayingolin Mongol Autonomous Prefecture, southern Yining City, and across all areas of Urumqi. The random forest model provides robust predictions of particulate matter concentrations, aiding in the understanding and mitigation of future pollution trends. This study offers valuable insights for atmospheric particulate matter pollution research in the Xinjiang region and serves as a reference for similar urban agglomerations. Full article

Particulate matter (PM) pollution is high in most Bulgarian regions, especially large urban areas. In a previous study covering one year of data collection and analysis by source apportionment techniques such as positive matrix factorization we show that the main source of high PM₁₀ (PM with a diameter of 10 μm or less) concentration in the city of Sofia is soil and road dust resuspension into the surface layer of the air. Resuspension has seasonal variations, with a relatively large impact (25%) associated with drying periods. In the present paper we combine classical indices (NDVI, BSI, NDMI) derived from Sentinel-2 imagery with meteorological and air quality data, as well as other related variables regarding yearly average traffic and inventory estimates, transportation infrastructure and demographic data, including motorized inhabitants and wood/coal stoves in use, by area. We apply statistical and machine learning methods to analyze the contribution of bare soil surfaces to the overall PM resuspension. Based on a series of stack ensemble meta-models with coefficient of determination $R^2\approx 0.9$ we conclude that the contribution of bare soil surfaces to the overall PM₁₀ resuspension is around $10\%$ (between $5\%$ and $15\%$), by our preliminary rough estimates. Full article

Accurate assessment of vehicular air pollution in arid urban environments remains a challenge because standard emission models often overlook localized influences such as climate-driven dust resuspension and urban canyon effects. This study develops an enhanced modeling framework that integrates critical regional parameters into established algorithms to improve estimates of traffic-related emissions, including PM₁₀, PM₂.₅, CO, and NO₂. The US EPA’s AP-42 algorithm was modified to incorporate a novel highway width-to-building height ratio (I/H) and a climate-driven dynamic silt loading model derived from satellite data, while the European EEA algorithm was refined by introducing an explicit fuel density correction (ρ). The framework was applied and validated on two representative highways in Jordan—an industrial corridor and an urban-commercial artery—using continuous sensor-based measurements. Results indicate substantial improvement in predictive performance, with reductions of 60–77% in normalized difference for particulate matter and 72% for CO. The model successfully distinguished between emission regimes, capturing a seasonal silt-loading peak of approximately 17.5 g/m² during autumn at the industrial site, compared to more stable, traffic-dominated emissions along the urban corridor. Although NO₂ performance showed modest gains (4–40%) due to complex photochemical processes, the overall framework proved to be a robust and reliable tool for air quality assessment in arid cities. This adaptable approach provides a foundation for targeted air pollution management, and future work will integrate real-time dispersion dynamics and photochemical modules to better capture secondary pollutant formation. Full article

Road dust contains a variety of heavy metals and is a widely used sustainability indicator for monitoring pollution and assessing environmental and health risks in sustainable development. Heavy metals in road dust mainly originate from worn-off particles from vehicles, such as tires, brake pads, road dust, and emissions from exhaust pipes. These heavy metal particles could remain on the road surface for a long period and cause environmental pollution. In this preliminary study, road dust was collected from 8 representative areas in Macao. The heavy metal content from road dust in Macao was extracted from each of the collected samples for an assessment of the heavy metal pollution and its potential threat to human health. The results show that heavy metals primarily originate from human activities, including transportation emissions (Mn: 67.37%, Zn: 57.01%, Sb: 54.1%) and industrial activities (Al: 84.70%, Fe: 76.71%, Pb: 65.32%). The metal-specific non-carcinogenic risk ranges from 1.17 × 10⁻⁷ to 2.65 × 10⁻⁵ and the total carcinogenic risk is 6.91 × 10⁻¹⁰, showing minimum health effects from heavy metals in road dust. Furthermore, there is a significant correlation between the total vehicle counts and the heavy metal contents such as Al, Si, As, V, and Fe (r = 0.50 to 0.82). This work represents the first characterization of heavy metal contents and risks of urban road dust in Macao. Full article

Urban populations in Latin America are highly exposed to traffic-related pollutants, yet monitoring networks remain limited. This study proposes a low-cost methodology to identify urban pollution hotspots in the city of Córdoba, Argentina, by categorizing 20 sites based on traffic categories using Google Traffic data. Measurements of PM_2.5, polycyclic aromatic hydrocarbons (PAHs), and equivalent sound pressure level (LA_eq) were conducted over a 21-day cold-season period. Mean PM_2.5 concentrations ranged from 7.5 to 27.3 µg/m³, and total PAHs ranged from 1.4 to 7.9 ng/m³. Sites with high and medium traffic density exhibited significantly higher PAH concentrations and noise levels, with LAeq₅ values exceeding 65 dB at all urban core locations. Conversely, PM_2.5 concentrations were higher at peripheral sites due to topography, dust resuspension, and wildfire events. Strong correlations were found between vehicular flow and noise (r = 0.94), and between heavy-vehicle proportion and noise (r = 0.60). The lifetime lung cancer risk associated with PAH exposure was classified as “low” according to USEPA criteria. This traffic-based categorization approach provides a rapid and cost-effective tool for identifying high-risk areas in resource-limited settings, supporting urban planning and public health interventions. Full article

This study assesses air quality and health impact in Hanoi, Vietnam, using the Community Multiscale Air Quality (CMAQ) model and health impact assessment to evaluate the effectiveness of traffic emission reduction strategies under two scenarios. An updated emission inventory was used as the input data for the CMAQ model. The Weather Research and Forecasting (WRF-CMAQ) model (version 5.4), incorporating the CB6 chemical mechanism, was applied alongside a calibrated meteorological model to simulate pollutant dispersion. The model achieved strong performance in PM_2.5 simulation, with a correlation coefficient (R) of 0.78, an index of agreement (IOA) of −0.5, a Normalized Mean Bias (NMB) of 7.11%, and a normalized mean error (NME) of 28.51%. Seasonal analysis revealed higher concentrations of CO, NO₂, O₃, and SO₂ in January compared to July, driven by traffic and industrial emissions. Improved air quality in July was attributed to favorable meteorological conditions, such as increased rainfall and clean airflows from the sea. Spatial distribution highlighted elevated pollutant levels in urban areas, while PM_2.5 was significantly influenced by long-range transport and atmospheric processes. However, fine dust concentrations remained high in suburban areas, driven by secondary emissions and nearby industrial zones. An emission reduction scenario based on the Hanoi city policy decree focusing on traffic sources demonstrated its potential to reduce NO₂, SO₂, and PM_2.5 concentrations, though the impacts varied across time and space. Health impact due to population exposure to PM_2.5 shows that the densely populated suburbs surrounding the urban core have the largest impact in terms of mortality and cardiovascular diseases hospitalization. As PM_2.5 has the largest impact on these two health endpoints, only PM_2.5 impact assessment is performed. Health impact due to air pollution is higher in January (dry season) with estimated 625 deaths and 124 cardiovascular diseases (cvd) hospitalization as compared with estimated 94 deaths and 18 cvd hospitalization in July (wet season). One of the research questions posed by the city authority is whether converting diesel buses to electric buses can yield environmental and health benefits. Our work shows that the scenario based on Hanoi city decree of replacing 50% of fossil fuel combustion buses with electric buses by 2035 does not yield perceptible change in mortality health effect. This is due to emission from buses being small as compared to those from the whole transport sector and other sectors. This study emphasizes the need for integrated, targeted emission control strategies to address spatial and temporal variability in pollution. The findings offer valuable insights for policymakers to develop effective measures in urban planning for improving air quality and protecting the health of people in Hanoi. Full article

Maintaining air quality is an important environmental challenge, affecting both urban and regional areas where industrial, agricultural, and energy activities intersect. The Upper Hunter Valley, NSW, experiences emissions from coal mining, power generation, agriculture, and wood fires, compounded by local meteorology, geology, and climate change. This study applies a political economy framework to examine historical governance structures including colonial legacies, institutional arrangements, and power relations and how they shape stakeholder roles and influence decision-making related to air quality. Technical applied research including improving dust monitoring, occupational health studies, and investigations into alternative fuels provided an empirical basis for identifying key stakeholders, including mining and energy companies, regulatory agencies, local councils, community groups, and environmental organisations. The analysis demonstrates how these actors influence governance processes, social licence to operate, and public perceptions of environmental risk. Findings indicate that effective air quality management requires multi-level, collaborative approaches that integrate technical expertise, regulatory oversight, and community engagement. The study highlights the importance of systemic strategies that align economic, environmental, and social objectives, providing insight into the governance of contested environmental resources in historically and politically complex regional contexts. This article is a rewritten and expanded version of the study “Analysis of air quality stakeholders in the Upper Hunter”, presented at the Clean Air conference, in Hobart, Australia, August 2024. Full article

This study, based on global AERONET observation data from 2023, employs a synergistic inversion algorithm that integrates aerosol optical, microphysical, and chemical properties to retrieve the global distribution of aerosol parameters. We find that the global annual mean aerosol optical depth (AOD), fine-mode AOD (AOD_f), coarse-mode AOD (AOD_c), absorbing aerosol optical depth (AAOD), single scattering albedo (SSA) are 0.20, 0.15, 0.04, 0.024, and 0.87, respectively. From the perspective of spatial distribution, in densely populated urban areas, AOD is mainly determined by AOD_f, while in the areas dominated by natural sources, AOD_c contributes more. Combined with the optical and microphysical properties, fine-mode aerosols dominate optical contributions, whereas coarse-mode aerosols dominate volume contributions. In terms of chemical components, fine-mode aerosols at most global sites are primarily carbonaceous. The mass concentrations of black carbon (BC) exceed 10 mg m⁻² in parts of South Asia, Southeast Asia, and the Arabian Peninsula, while the mass fraction of brown carbon (BrC) accounts for more than 16% in regions such as the Sahara, Western Africa, and the North Atlantic Ocean reference areas. The dust (DU) dominates in coarse mode, with the annual mean DU fraction reaching 86.07% in the Sahara. In coastal and humid regions, the sea salt (SS) and water content (AW_c) contribute significantly to the aerosol mass, with fractions reaching 13.13% and 34.39%. The comparison of aerosol properties in the hemispheres reveals that the aerosol loading in the Northern Hemisphere caused by human activities is higher than in the Southern Hemisphere, and the absorption properties are also stronger. We also find that the uneven distribution of global observation sites leads to a significant underestimation of aerosol absorption and coarse-mode features in global mean values, highlighting the adverse impact of observational imbalance on the assessment of global aerosol properties. By combining analyses of aerosol optical, microphysical, and chemical properties, our study offers a quantitative foundation for understanding the spatiotemporal distribution of global aerosols and their emission contributions, providing valuable insights for climate change assessment and air quality research. Full article