Search Results (621)

Soil fugitive dust (SFD) emissions pose a significant threat to both human health and the environment, highlighting the need for accurate and reliable estimation and assessment in the desert regions of northwest China. This study used climate, soil, and vegetation data from Bayingolin Prefecture (2001–2022) and applied the WEQ model to analyze temporal and spatial variations in total suspended particulate (TSP), PM₁₀, and PM_2.5 emissions and their driving factors. The region exhibited high emission factors for TSP, PM₁₀, and PM_2.5, averaging 55.46 t km⁻² a⁻¹, 27.73 t km⁻² a⁻¹, and 4.14 t km⁻² a⁻¹, respectively, with pronounced spatial heterogeneity and the highest values observed in Yuli, Qiemo, and Ruoqiang. The annual average emissions of TSP, PM₁₀, and PM_2.5 were 3.23 × 10⁷ t, 1.61 × 10⁷ t, and 2.41 × 10⁶ t, respectively. Bare land was the dominant source, contributing 72.55% of TSP emissions. Both total emissions and emission factors showed an overall upward trend, reaching their lowest point around 2012, followed by significant increases in most counties during 2012–2022. Annual precipitation, wind speed, and temperature were identified as the primary climatic drivers of soil dust emissions across all counties, and their influences exhibited pronounced spatial heterogeneity in Bazhou. In Ruoqiang, Bohu, Korla, and Qiemo, dust emissions are mainly limited by precipitation, although dry conditions and sparse vegetation can amplify the role of wind. In Heshuo, Hejing, and Yanqi, stable vegetation helps to lessen wind’s impact. In Yuli, wind speed and temperature are the main drivers, whereas in Luntai, precipitation and temperature are both important constraints. These findings highlight the need to consider emission intensity, land use, or surface condition changes, and the potential benefits of increasing vegetation cover in severely desertified areas when formulating regional dust mitigation strategies. Full article

Solid fuels are still widely used in household heating in Europe and North America. Emissions from boilers are released in proximity to people. Therefore, there is a need to minimise the toxicity of emissions affecting human health to the greatest extent possible. This study compares the genotoxic potential of the emissions of four boilers of modern and old design (automatic, gasification, down-draft, over-fire) operating at reduced output to simulate the real-life combustion fed by various fossil and renewable solid fuels (hard coal, brown coal, brown coal briquettes, wood pellets, wet and dry spruce). Organic emissions were tested for genotoxic potential by analysing bulky DNA adducts and 8-oxo-dG adduct induction. There was no consistent genotoxic pattern among the fuels used within the boilers. Genotoxicity was strongly correlated with polycyclic aromatic hydrocarbon (PAH) content, and even stronger correlation was observed with particulate matter (PM). In all measured variables (PM, PAHs, genotoxicity), the technology of the boilers was a more important factor in determining the genotoxic potential than the fuels burned. The highest levels of both bulky and 8-oxo-dG DNA adducts were induced by organics originating from the over-fire boiler, while the automatic boiler exhibited genotoxic potential that was ~1000- and 100-fold lower, respectively. Full article

PM_2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) pollution is one of the most common problems triggered by the acceleration of urbanization. The coordinated development of cities and the environment has been a topic of significant interest in recent years. Based on the spatiotemporal relationship between the evolution of urbanization levels and PM_2.5 concentrations, and starting from multiple factors characterizing urbanization, this study constructs a coupling coordination degree model between PM_2.5 and urbanization levels to explore the interaction and degree of coordination between urbanization and PM_2.5 in Guangdong Province from 2000 to 2021. The research reveals that the conflict between the urbanization process and PM_2.5 pollution in various cities of Guangdong Province is gradually easing. The year 2011 was a turning point as the PM_2.5 pollution levels in cities that were in an uncoordinated phase began to improve. The coupling coordination degree between urbanization and PM_2.5 pollution in Guangdong Province exhibits significant spatial heterogeneity. The coupling coordination degree in most coastal cities is higher than that in inland cities. Cities in economically underdeveloped regions also face relatively lower pressure from pollution emissions. These regions are characterized by lagging urbanization, and their coupling coordination degree is slowly increasing as urbanization progresses. In economically developed regions, the coupling coordination degree between urbanization levels and PM_2.5 pollution has reached a basic level of coordination, although the specific types vary. Full article

Ambient PM concentrations are influenced by various emission sources and weather conditions such as temperature, wind speed, and direction. Measurements using optical sensors cannot directly link pollution levels to specific sources. Data from roadside monitoring often show that a significant portion of PM originates from non-traffic sources. Therefore, vehicle-related PM emissions are typically estimated using simulation models based on average emission factors. This study uses the COPERT (Computer Programme to Calculate Emissions from Road Transport) model to estimate emissions from road vehicles under current conditions and future scenarios. These include the introduction of Euro 7 standards and a shift from internal combustion engine (ICE) vehicles to battery electric vehicles (BEVs). The analysis considers exhaust and non-exhaust emissions, as well as indirect emissions from electricity generation for BEV charging. The conducted study showed, among other findings, that replacing internal combustion engine vehicles with electric ones could reduce PM2.5 emissions by approximately 6% (2% when including indirect emissions from electricity generation) and PM10 emissions by about 10% (5% with indirect emissions), compared to the Euro 7 scenario. Full article

Renewable energy stands as a pivotal solution to environmental concerns, prompting substantial research and development endeavors to promote its adoption and enhance energy efficiency. Despite the recognized environmental superiority of renewable energy systems, there is a lack of globally standardized indicators specifically focused on renewable energy efficiency. This study aims to develop and apply a non-parametric data envelopment analysis (DEA) indicator, termed the Renewable Energy Indicator (REI), to measure environmental performance at the national level and to identify differences in renewable energy efficiency across countries grouped by development status and income level. The REI incorporates new factors such as agricultural methane emissions (thousand metric tons of ${\mathrm{CO}}_2$ equivalent), PM2.5 air pollution exposure (µg/m³), and aspects related to electricity, including consumption (as % of total final energy consumption), production from renewable sources, excluding hydroelectric (kWh), and accessibility in rural and urban areas (% of population with access), aligning with the emerging paradigm outlined by the United Nations. By segmenting the REI into global, developmental, and income group classifications, this study conducts the Mann–Whitney U test and the Kruskal–Wallis H tests to identify variations in renewable energy efficiency among different country groups. Our findings reveal top-performing countries globally, highlighting both developed (e.g., Sweden) and developing nations (e.g., Costa Rica, Sri Lanka). Central and North European countries demonstrate high efficiency, while those facing political and economic instability perform poorly. Agricultural-dependent nations like Australia and Argentina exhibit lower REI due to significant methane emissions. Disparities between developed and developing markets underscore the importance of understanding distinct socio-economic dynamics for effective policy formulation. Comparative analysis across income groups informs specific strategies tailored to each category. Full article

Environmental comfort and air pollution are among the most important indicators for assessing the population’s quality of life in urban agglomerations. This study aims to explore long-term memory in air pollution time series by analyzing the dynamics of the Hurst exponent and evaluating the predictability index. This type of statistical pre-forecast analysis is essential for developing accurate forecasting models for such time series. The effectiveness of air quality monitoring systems largely depends on the precision of these forecasts. The Ekibastuz coal-mining center, which houses one of the largest coal-fired power stations in Kazakhstan and the world, with a capacity of about 4000 MW, was chosen as an example for the study. Data for the period from 1 March 2023 to 31 December 2024 were collected and analyzed at the Ekibastuz coal-fired power station. During the specified period, 14 indicators (67,527 observations) were collected at 10 min intervals, including mass concentrations of CO, NO, NO₂, SO₂, PM_2.5, and PM₁₀, as well as current mass consumption of CO, NO, NO₂, SO₂, dust, and NO_x. The detrended fluctuation analysis of a time series of air pollution indicators was used to calculate the Hurst exponent and identify long-term memory. Changes in the Hurst exponent in regards to dynamics were also investigated, and a predictability index was calculated to monitor emissions of pollutants in the air. Long-term memory is recorded in the structure of all the time series of air pollution indicators. Dynamic analysis of the Hurst exponent confirmed persistent time series characteristics, with an average Hurst exponent of about 0.7. Identifying the time series plots for which the Hurst exponent is falling (analysis of the indicator of dynamics), along with the predictability index, is a sign of an increase in the influence of random factors on the time series. This is a sign of changes in the dynamics of the pollutant release concentrations and may indicate possible excess emissions that need to be controlled. Calculating the dynamic changes in the Hurst exponent for the emission time series made it possible to identify two distinct clusters corresponding to periods of persistence and randomness in the operation of the coal-fired power station. The study shows that evaluating the predictability index helps fine-tune the parameters of time series forecasting models, which is crucial for developing reliable air pollution monitoring systems. The results obtained in this study allow us to conclude that the method of trended fluctuation analysis can be the basis for creating an indicator of the level of air pollution, which allows us to quickly respond to possible deviations from the established standards. Environmental services can use the results to build reliable monitoring systems for air pollution from coal combustion emissions, especially near populated areas. Full article

The impact of black carbon (BC) emissions on climate change, human health, and the environment is well-documented in the scientific literature. Although BC still remains largely unregulated at the international level, efforts have been made to reduce emissions of BC and Particulate Matter (PM_2.5), particularly in sectors such as energy production, industry, and road transport. In contrast, the maritime shipping industry has made limited progress in reducing BC emissions from ships, mainly due to the absence of stringent BC emission regulations. While the International Maritime Organization (IMO) has established emission limits for pollutants such as SO_x, NO_x, and VOCs under MARPOL Annex VI, as of today, BC emissions from ships are still unregulated at the international level. Whereas it was anticipated that PM_2.5 and BC emissions would be reduced with the adoption of the SO_x regulations, especially within the sulfur emission control areas (SECA), this study reveals that BC emissions are only partially affected by the current MARPOL Annex VI regulations. Based on 886 real-world black carbon (BC) emission measurements from ships operating in the southern North Sea, the study demonstrates that SECA-compliant fuels do contribute to a notable decrease in BC emissions. However, it is important to note that the average BC emission factors (EFs) within the SECA remain comparable in magnitude to those reported for non-compliant fuels in earlier studies. Moreover, ships using exhaust gas cleaning systems (EGCSs) as a SECA-compliant measure were found to emit significantly higher levels of BC, raising concerns about the environmental sustainability of EGCSs as an emissions mitigation strategy. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

This study analyzes the spatial and temporal variability of PM₁₀ and PM_2.5 concentrations in Santo Domingo, Dominican Republic, based on short-term sampling campaigns conducted in 2019 and 2022. In 2019, PM₁₀ levels averaged 38.14 µg/m³, while in 2022 they rose significantly to 62.18 µg/m³. PM_2.5 in 2022 averaged 30.37 µg/m³. These differences are likely influenced by meteorological variability, including increased transport of Saharan dust in mid-2022, and seasonal factors. Although local emission changes were not directly assessed, they may have also played a role in the observed trends. Statistical analyses revealed that aerosol optical depth (AOD), air pressure, and rainfall were significant predictors of PM₁₀ in 2022, explaining up to 75% of the variance. Correlations and regression models confirmed a robust association between AOD and PM levels on a weekly timescale. These findings highlight the importance of integrating remote sensing and meteorological data to improve air quality monitoring and inform environmental policy in Caribbean urban areas. Full article

The Tula Metropolitan Area in Mexico is characterized by significant industrial activity, including thermoelectric power plants, refineries, cement plants, and mining operations. While the impact of mining on air quality has been less studied compared to other industries, this research aims to estimate the contribution of mining areas to PM₁₀ air pollution in the region. Using the AERMOD dispersion model coupled with the WRF meteorological model, emission areas were identified through GIS analysis, and specific emission factors for mining activities were applied. The results indicate that mining areas can contribute up to 40 µg/m³ of PM₁₀, exceeding both national and international air quality standards. Monitoring data suggests that mining activities account for approximately 30% of the measured PM₁₀ concentrations in the area. Furthermore, spatial analysis using the Urban Marginalization Index (UMI) revealed that areas with high PM₁₀ concentrations often coincide with regions of high social vulnerability, particularly in communities with elevated levels of marginalization. This study concludes that mining operations significantly contribute to air pollution in the Tula Metropolitan Area, highlighting the need for targeted mitigation measures and public policies that address both environmental and social vulnerabilities. Full article

Carbon emissions from global warming pose significant threats to both regional ecology and sustainable development. Understanding the factors affecting emissions is critical to developing effective carbon neutral strategies. This study constructed a precise 1 km resolution net carbon emissions map of Hubei Province, China (2000–2020), and compared the ten distinct machine learning models to identify the most effective model for revealing the relationship between carbon emissions and their influencing factors. The random forest regressor (RFR) demonstrates optimal performance, achieving root mean square error (RMSE) and mean absolute error (MAE) values that are nearly 10 times lower on average than the other models. The results are interpreted using Shapley additive explanation (SHAP), revealing dynamic factor impacts. Our findings include the following. (1) Between 2000 and 2020, net carbon emissions in Hubei increased threefold, with emissions from construction land rising by approximately 7.5 times over the past two decades. Woodland, a major carbon sink, experienced a downward trend. (2) Six key factors are population, the normalized difference vegetation index (NDVI), road density, PM_2.5, the degree of urbanization, and the industrial scale, with only the NDVI reducing emissions. (3) Net carbon emissions displayed significant spatial differences and aggregation and are mainly concentrated in the central urban areas of Hubei Province. Overall, this study evaluates various regression models and identifies the primary factors influencing net carbon emissions. The net carbon emission map we have developed can visually identify and locate high-emission hotspots and vulnerable carbon sink areas, thereby providing a direct basis for provincial land use planning. Full article

Although the transition to electric vehicles (EVs) is well underway and expected to continue in global car markets, most vehicles on the world’s roads will be powered by internal combustion engine vehicles (ICEVs) and fossil fuels for the foreseeable future, possibly well past 2050. Thus, good environmental performance and effective emission control of ICE vehicles will continue to be of paramount importance if the world is to achieve the stated air and climate pollution reduction goals. In this study, we review 228 publications and identify four main issues confronting these objectives: (1) cheating by vehicle manufacturers, (2) tampering by vehicle owners, (3) malfunctioning emission control systems, and (4) inadequate in-service emission programs. With progressively more stringent vehicle emission and fuel quality standards being implemented in all major markets, engine designs and emission control systems have become increasingly complex and sophisticated, creating opportunities for cheating and tampering. This is not a new phenomenon, with the first cases reported in the 1970s and continuing to happen today. Cheating appears not to be restricted to specific manufacturers or vehicle types. Suspicious real-world emissions behavior suggests that the use of defeat devices may be widespread. Defeat devices are primarily a concern with diesel vehicles, where emission control deactivation in real-world driving can lower manufacturing costs, improve fuel economy, reduce engine noise, improve vehicle performance, and extend refill intervals for diesel exhaust fluid, if present. Despite the financial penalties, undesired global attention, damage to brand reputation, a temporary drop in sales and stock value, and forced recalls, cheating may continue. Private vehicle owners resort to tampering to (1) improve performance and fuel efficiency; (2) avoid operating costs, including repairs; (3) increase the resale value of the vehicle (i.e., odometer tampering); or (4) simply to rebel against established norms. Tampering and cheating in the commercial freight sector also mean undercutting law-abiding operators, gaining unfair economic advantage, and posing excess harm to the environment and public health. At the individual vehicle level, the impacts of cheating, tampering, or malfunctioning emission control systems can be substantial. The removal or deactivation of emission control systems increases emissions—for instance, typically 70% (NO_x and EGR), a factor of 3 or more (NO_x and SCR), and a factor of 25–100 (PM and DPF). Our analysis shows significant uncertainty and (geographic) variability regarding the occurrence of cheating and tampering by vehicle owners. The available evidence suggests that fleet-wide impacts of cheating and tampering on emissions are undeniable, substantial, and cannot be ignored. The presence of a relatively small fraction of high-emitters, due to either cheating, tampering, or malfunctioning, causes excess pollution that must be tackled by environmental authorities around the world, in particular in emerging economies, where millions of used ICE vehicles from the US and EU end up. Modernized in-service emission programs designed to efficiently identify and fix large faults are needed to ensure that the benefits of modern vehicle technologies are not lost. Effective programs should address malfunctions, engine problems, incorrect repairs, a lack of servicing and maintenance, poorly retrofitted fuel and emission control systems, the use of improper or low-quality fuels and tampering. Periodic Test and Repair (PTR) is a common in-service program. We estimate that PTR generally reduces emissions by 11% (8–14%), 11% (7–15%), and 4% (−1–10%) for carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NO_x), respectively. This is based on the grand mean effect and the associated 95% confidence interval. PTR effectiveness could be significantly higher, but we find that it critically depends on various design factors, including (1) comprehensive fleet coverage, (2) a suitable test procedure, (3) compliance and enforcement, (4) proper technician training, (5) quality control and quality assurance, (6) periodic program evaluation, and (7) minimization of waivers and exemptions. Now that both particulate matter (PM, i.e., DPF) and NO_x (i.e., SCR) emission controls are common in all modern new diesel vehicles, and commonly the focus of cheating and tampering, robust measurement approaches for assessing in-use emissions performance are urgently needed to modernize PTR programs. To increase (cost) effectiveness, a modern approach could include screening methods, such as remote sensing and plume chasing. We conclude this study with recommendations and suggestions for future improvements and research, listing a range of potential solutions for the issues identified in new and in-service vehicles. Full article

This study presented a comprehensive physical–statistical analysis of atmospheric particulate matter (PM₁₀ and PM_2.5) and trace gases (SO₂ and O₃) over Faial Island in the Azores archipelago during 2024. We collected real-time data at the Espalhafatos rural background station, covering 35,137 observations per pollutant, with 15 min intervals. Descriptive statistics, probability distribution fitting (Normal, Lognormal, Weibull, Gamma), and correlation analyses were employed to characterize pollutant dynamics and identify extreme pollution episodes. The results revealed that PM_2.5 (fine particles) concentrations are best modeled by a Lognormal distribution, while PM₁₀ concentrations fit a Gamma distribution, highlighting the presence of heavy-tailed, positively skewed behavior in both cases. Seasonal and episodic variability was significant, with multiple Saharan dust transport events contributing to PM exceedances, particularly during winter and spring months. These events, confirmed by CAMS and SKIRON dust dispersion models, affected not only southern Europe but also the Northeast Atlantic, including the Azores region. Weak to moderate correlations were observed between PM concentrations and meteorological variables, indicating complex interactions influenced by atmospheric stability and long-range transport processes. Linear regression analyses between SO₂ and O₃, and between SO₂ and PM_2.5, showed statistically significant but low-explanatory relationships, suggesting that other meteorological and chemical factors play a dominant role. This result highlights the importance of developing air quality policies that address both local emissions and long-range transport phenomena. They support the implementation of early warning systems and health risk assessments based on probabilistic modeling of particulate matter concentrations, even in remote Atlantic locations such as the Azores. Full article

Ammonium nitrate (NH₄NO₃) is a major constituent of fine particulate matter (PM_2.5), playing a critical role in air quality and atmospheric chemistry. However, the dual regulatory role of ammonia (NH₃) in both the formation and volatilization of NH₄NO₃ under ambient atmospheric conditions remains inadequately understood. To address this gap, we conducted high-resolution field measurements at a clean tropical coastal site in China using an integrated system of Aerosol Ion Monitor-Ion Chromatography, a Scanning Mobility Particle Sizer, and online OC/EC analyzers. These observations were complemented by thermodynamic modeling (E-AIM) and source apportionment via a Positive Matrix Factorization (PMF) model. The E-AIM simulations revealed persistent thermodynamic disequilibrium, with particulate NO₃⁻ tending to volatilize even under NH_3gas-rich conditions during the northeast monsoon. This suggests that NH₄NO₃ in PM_2.5 forms rapidly within fresh combustion plumes and/or those modified by non-precipitation clouds and then undergoes substantial evaporation as it disperses through the atmosphere. Under the southeast monsoon conditions, reactions constrained by sea salt aerosols became dominant, promoting the formation of particulate NO₃⁻ while suppressing NH₄NO₃ formation despite ongoing plume influence. In scenarios of regional accumulation, elevated NH₃ concentrations suppressed NH₄NO₃ volatilization, thereby enhancing the stability of particulate NO₃⁻ in PM_2.5. PMF analysis identified five source factors, with NO₃⁻ in PM_2.5 primarily associated with emissions from local power plants and the large-scale regional background, showing marked seasonal variability. These findings highlight the complex and dynamic interplay between the formation and evaporation of NH₄NO₃ in NH_3gas-rich coastal atmospheres. Full article

This study investigates the concentrations, health risks, and potential sources of heavy metal elements and polycyclic aromatic hydrocarbons (PAHs) in PM_1.1 particles in Zhuji, a major copper-processing city in China. The ratios of heavy metals (summer: 0.906; winter: 0.619) and PAHs (>0.750 in both seasons) in PM_1.1/PM_2.0 suggest significant accumulation in ultrafine particles. In winter, heavy metal concentrations in PM_1.1 reached up to 448 ng/m³, and PAH concentrations were 13.4 ng/m³—over ten times higher than in summer. Health risk assessments revealed that hazard index (HI) values exceeded 1.00 for five age groups (excluding infants) during winter, indicating chronic exposure risks. Incremental lifetime cancer risk (ILCR) values surpassed the upper acceptable limit (1.0 × 10⁻⁴) for four age groups, with Cr, As, Cd, and Pb as major contributors. PAH-related ILCRs were also elevated in winter, with benzo[a]pyrene (BaP) identified as the most potent carcinogen. Enrichment factor (EF) and principal component analysis (PCA) indicated that industrial activities and traffic emissions were the dominant anthropogenic sources of heavy metals. Diagnostic ratio analysis further showed that PAHs mainly originated from vehicle and coal combustion. These findings provide critical insights into pollution patterns in industrial cities and underscore the importance of targeted mitigation strategies. Full article