Search Results (493)

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO₂, 20,500 tons of NO_x, 4250 tons of SO₂, 656 tons of PM₁₀, and 603 tons of PM_2.5, with anchorage activities contributing 497,000 tons of CO₂, 7010 tons of NO_x, 1520 tons of SO₂, 232 tons of PM₁₀, and 214 tons of PM_2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies. Full article

California’s San Joaquin Valley experiences some of the worst particulate matter (PM) air pollution in the U.S., but PM_2.5 and PM₁₀ exposures in agricultural communities are understudied. We collaborated with rural residents living adjacent to large-scale agricultural production and processing activities to assess 24-h-average personal and indoor PM_2.5 and PM₁₀ concentrations during different seasons. We visited 35 participants from 18 households during December 2023, May 2024, and the September 2024 harvest season to collect PM samples and survey data. Mixed effects linear regression models (with random effects for participant or household) assessed associations between natural log-transformed PM concentrations and regional ambient PM, harvest season, as well as participant/household characteristics. Participants were mostly female (69%) and Hispanic/Latino(a) (100%). Median household distance to processing facility silos was 633 m. Median personal exposures to PM_2.5 and PM₁₀ were 11.1 and 45.5 µg m⁻³. Median indoor PM_2.5 and PM₁₀ levels were 12.9 and 24.3 µg m⁻³. Overall, 29% of personal and indoor PM_2.5 samples and 33% of personal and indoor PM₁₀ samples exceeded WHO 24-h air quality guidelines (15 µg m⁻³ PM_2.5, 45 µg m⁻³ PM₁₀). The factors most strongly associated with personal and indoor PM were household members working in agriculture and regional ambient PM measures. Full article

Accurate identification of bare soil exposure and quantification of associated dust emissions are essential for understanding land degradation and air quality risks in intensively farmed regions. This study develops a monthly monitoring and modeling framework to quantify bare soil dynamics and wind erosion-induced particulate matter (PM) emissions across Shandong Province from 2017 to 2024. By integrating Sentinel-1/2 imagery, climate reanalysis, terrain and soil data, and employing a stacking ensemble classification model, we mapped bare soil areas at 10 m resolution with an overall accuracy of 93.1%. The results show distinct seasonal variation, with bare soil area peaking in winter and early spring, exceeding 25,000 km² or 15% of the total area, which is far above the 6.4% estimated by land cover products. Simulations using the CLM5.0 dust module indicate that annual PM₁₀ emissions from bare soil averaged (2.72 ± 1.09) × 10⁵ tons across 2017–2024. Emissions were highest in March and lowest in summer months, with over 80% of the total emitted during winter and spring. A notable increase in emissions was observed after 2022, likely due to more frequent extreme wind events. Spatially, emissions were concentrated in coastal lowlands such as the Yellow River Delta and surrounding saline–alkali lands. Our approach explicitly advances traditional methods by generating monthly 10 m bare soil maps and linking satellite-derived dynamics with process-based dust emission modeling, providing a robust basis for targeted dust control and land management strategies. Full article

This study evaluates occupational exposure to respirable particulate matter (PM2.5) and crystalline silica (c-silica) among workers in five ceramic industries in Indonesia. Personal sampling revealed that 55.3% of workers were exposed to c-silica levels exceeding the Threshold Limit Value (TLV) of 50 µg/m³, with concentrations ranging from 1.5 to 1395.3 µg/m³. PM2.5 levels reached as high as 4152.4 µg/m³ in certain production zones. Health surveys identified frequent respiratory symptoms such as shortness of breath (27.1%) and chronic cough (14.6%), with 6.4% of workers showing lung abnormalities on chest X-rays. Risk assessments based on chronic daily intake (CDI), hazard quotient (HQ), and risk quotient (RQ) revealed that 63.8% of workers faced unsafe exposure, particularly those with longer job tenures, older age, and poor compliance with personal protective equipment (PPE). To mitigate risks, the study recommends engineering controls such as more local exhaust ventilation, improved PPE usage, and administrative measures including job rotation and regular health monitoring. These findings highlight the urgent need for improved occupational health strategies in silica-intensive industries and call for further research on long-term health impacts and effective intervention programs. Full article

Indoor air pollution remains a critical health issue in the rural areas of low- and middle-income countries like Indonesia, where solid fuels are commonly used for cooking. This study assessed real-time indoor particulate matter (PM) concentrations in three rural households in Jorong V Botung, West Sumatra, using PurpleAir low-cost sensors (PurpleAir Inc., Draper, UT, USA). Mass concentrations of PM₁, PM_2.5, and PM₁₀, along with size-segregated number concentrations (0.3–10 µm), were monitored continuously over six days (30 March–4 April 2024) during the Eid al-Fitr holiday, which involves extensive cooking activities. PM_2.5 concentrations frequently exceeded 200 µg/m³, with a peak of 249.9 µg/m³ recorded during morning cooking hours. The smallest particle size (0.3–0.5 µm) dominated number concentrations, reaching up to 17,098 particles/dL, while larger particle levels were significantly lower. Strong positive correlations (r > 0.95) were observed among PM₁, PM_2.5, PM₁₀ and AQI, indicating that cooking emissions substantially contributed to indoor PM levels. The findings highlight the need for targeted interventions, including clean fuel subsidies, improved ventilation, and public awareness efforts. This study contributes critical data on indoor air quality in rural Indonesia and supports broader initiatives to reduce exposure to household air pollution in Southeast Asia. Full article

This study investigates the dynamics and sources of atmospheric mercury in Qinhuangdao (QHD), a coastal urban area significantly impacted by both marine and terrestrial sources. Sampling of gaseous elemental mercury (GEM), fine particle-bound mercury (PBM_2.5), and coarse particle-bound mercury (PBM_2.5–10) was conducted from September 2022 to August 2023. The annual mean concentrations of GEM, PBM_2.5, and PBM_2.5–10 were 2.66, 1.01, and 0.73 ng m⁻³, respectively, with PBM levels among the highest reported for coastal cities in eastern China. GEM displayed a pronounced midday peak (12:00–14:00) with correlations to temperature (R² = 0.25–0.65) and a significant winter association with SO₂ (R² = 0.52), suggesting the combined influence of surface re-emission and coal combustion. Seasonal variations in the GEM/CO ratio (spring: 7.12; winter: 2.62) further reflected the shift between natural and combustion-related sources. PBM_2.5 exhibited elevated concentrations (1.0–1.4 ng m⁻³) under westerly winds (~3 m s⁻¹), indicating inputs from traffic, shipping, and light industries, while PBM_2.5–10 (0.5–1.1 μg m⁻³) was strongly linked to coal-handling activities at QHD port and soil resuspension. Backward trajectory analysis showed continental air masses dominated in winter (53–100%) and maritime air masses in summer (30–50%), whereas high Hg/Na ratios in PM_2.5 (3.22 × 10⁻⁴) and PM_2.5–10 (2.17 × 10⁻⁴), far exceeding typical marine aerosol values (10⁻⁷–10⁻⁵), indicated negligible marine contributions to PBM. These findings provide new insights into the processes driving mercury pollution in coastal urban environments and highlight the critical role of port-related activities in regional mercury management. Full article

Shearer drum cutting of coal seams generates over half of the coal dust in coal mines, while relevant studies focus more on micron-sized dust and much less on nano- to sub-micron-sized coal dust. Based on the self-developed experimental system for simulating dust generation from drum cutting of coal bodies, this study investigated the concentration distribution characteristics and physicochemical properties of 10 nm–10 μm coal dust generated from drum cutting of different rank coals with different cutting parameters. Results showed that the coal dust mass and number concentrations were concentrated in 2–10 μm and 10–200 nm, respectively, accounting for 90% of the total 10 nm–10 μm coal dust; the mass percentages of PM1/PM10 (PM1/PM10 = PM1 particles relative to PM10 particles, similarly hereinafter), PM1/PM2.5, and PM2.5/PM10 were 3.25–4.87%, 19.35–26.73%, and 14.82–18.81%, respectively, whereas over 99% of the total number of particles in the PM10 fraction are within the PM1 fraction (i.e., N-PM1/N-PM10 > 99%), that is, both N-PM1/N-PM2.5 and N-PM2.5/N-PM10 exceeded 99%. Lower-rank coal generates less 10 nm–10 μm coal dust, and either higher moisture content, firmness coefficient, or lower fixed carbon content of the coal can effectively reduce the 10 nm–10 μm coal dust generation. Either reduction in the tooth tip cone angle, the rotary speed, or increase in the mounting angle or the cutting depth can effectively inhibit the 10 nm–10 μm coal dust generation. Higher-rank coal dust shows fewer surface pores, smoother surfaces, larger contact angles, more hydrophobic groups, and fewer hydrophilic groups. The research results have filled the knowledge gap in the pollution characteristics of nano- to submicron-sized dust generated from shearer drum cutting of coal bodies, and can serve as an important reference for the development of dust reduction and suppression technologies in coal mining faces as well as the prevention of coal worker’s pneumoconiosis. Full article

Reliable, high-resolution emission inventories are essential for accurately simulating air quality and for designing evidence-based mitigation policies. Yet their performance over Japan—where transboundary inflow, strict fuel regulations, and complex source mixes coexist—remains poorly quantified. This study therefore benchmarks four widely used anthropogenic inventories—REAS v3.2.1, CAMS-GLOB-ANT v6.2, ECLIPSE v6b, and HTAP v3—by coupling each to WRF-Chem (10 km grid) and comparing simulated surface PM₂.₅, SO₂, CO, and NO_x with observations from >900 stations across eight Japanese regions for the years 2010 and 2015. All simulations shared identical meteorology, chemistry, and natural-source inputs (MEGAN 2.1 biogenic VOCs; FINN v1.5 biomass burning) so that differences in model output isolate the influence of anthropogenic emissions. HTAP delivered the most balanced SO₂ and CO fields (regional mean biases mostly within ±25%), whereas ECLIPSE reproduced NO_x spatial gradients best, albeit with a negative overall bias. REAS captured industrial SO₂ reliably but over-estimated PM₂.₅ and NO_x in western conurbations while under-estimating them in rural prefectures. CAMS-GLOB-ANT showed systematic biases—under-estimating PM₂.₅ and CO yet markedly over-estimating SO₂—highlighting the need for Japan-specific sulfur-fuel adjustments. For several pollutant–region combinations, absolute errors exceeded 100%, confirming that emissions uncertainty, not model physics, dominates regional air quality error even under identical dynamical and chemical settings. These findings underscore the importance of inventory-specific and pollutant-specific selection—or better, multi-inventory ensemble approaches—when assessing Japanese air quality and formulating policy. Routine assimilation of ground and satellite data, together with inverse modeling, is recommended to narrow residual biases and improve future inventories. Full article

Indoor and outdoor air quality in school environments varies significantly with respect to particulate matter (PM) concentrations, carbon dioxide (CO₂) levels, and microclimatic conditions, all of which have a direct impact on the health, well-being, and performance of both students and staff. This study reports the findings of a monitoring campaign focused on PM₁₀ and PM_2.5 concentrations in two schools located in the urban area of Belgrade, Serbia. Measurements were carried out using low-cost sensor devices positioned in classrooms and in the surrounding outdoor environment. The PM concentration data were corrected through collocation with reference-grade automatic analyzers (Grimm EDM 180) from the National Air Quality Monitoring Network (NAQMN). During the winter season, the indoor-to-outdoor (I/O) concentration ratio for classrooms ranged between 0.7 and 0.8, indicating that indoor PM levels were generally lower than outdoor levels—likely a result of limited ventilation and reduced particle infiltration from outdoor sources. Conversely, in the summer season, the average I/O ratio typically exceeded 1.0 (ranging from 1.3 to 1.5), pointing to a more pronounced influence of indoor sources, such as occupant activities, resuspension of settled dust, and insufficient air exchange. Importantly, in over 60% of the measurements conducted during the summer period, indoor PM concentrations surpassed those outdoors, underscoring the critical need to address indoor emission sources and implement effective ventilation strategies, particularly during warmer months. Full article

The article addresses the issue of liquefied natural gas (LNG) distribution as a marine fuel, analyzing both ecological and economic aspects in the context of the Baltic Sea basin. The authors emphasize that LNG plays an increasingly significant role in the global energy balance, and its application in maritime transport is crucial for implementing sustainable development policies and advancing the energy transition. From an ecological perspective, LNG offers a substantial reduction in harmful emissions compared with conventional marine fuels such as marine diesel oil (MDO) and heavy fuel oil (HFO). In particular, the use of LNG results in lower emissions of carbon dioxide (CO₂), sulfur oxides (SO_x), nitrogen oxides (NO_x), and particulate matter (PM). The reduction in these pollutants is essential not only for improving air quality in port and coastal areas but also for mitigating global environmental impacts, including climate change. On the economic side, the article focuses on the cost structure of LNG distribution, highlighting that its price dynamics are subject to significant fluctuations. These variations are driven by geopolitical developments, crude oil price volatility, environmental regulations, and the expansion of bunkering infrastructure. From an economic perspective, LNG prices show significant volatility depending on the year and market conditions. Between 2018 and 2023, LNG prices ranged from approximately 450 to 530 USD/ton, at times exceeding the cost of HFO (400–550 USD/ton) but in some years remaining cheaper. In comparison, MDO prices were consistently the highest, increasing over the analyzed period from 600 USD/ton to over 720 USD/ton. This variability results from geopolitical factors, supply and demand dynamics, and environmental regulations, which highlight the strategic importance of LNG as a transitional fuel in shipping. The purpose of this study is to examine the role of LNG as an alternative fuel for shipping in the Baltic Sea, with particular emphasis on the scale of vessel emissions and the key factors influencing its distribution costs. Full article

Laboratory buildings represent some of the highest energy-consuming infrastructure due to stringent environmental requirements and the continuous operation of specialized equipment. Ensuring both energy efficiency and indoor air quality (IAQ) in such spaces remains a central challenge for sustainable building design and operation. Recent advances in Internet of Things (IoT) systems allow for real-time monitoring of multivariate environmental parameters, including CO₂, total volatile organic compounds (TVOC), PM_2.5, temperature, humidity, and noise. However, these datasets are often noisy or incomplete, complicating conventional monitoring approaches. Supervised anomaly detection methods are ill-suited to such contexts due to the lack of labeled data. In contrast, unsupervised machine learning (ML) techniques can autonomously detect patterns and deviations without annotations, offering a scalable alternative. The challenge of identifying anomalous environmental conditions and latent operational states in laboratory environments is addressed through the application of unsupervised models to 1808 hourly observations collected over four months. Anomaly detection was conducted using Isolation Forest (300 trees, contamination = 0.05) and One-Class Support Vector Machine (One-Class SVM) (RBF kernel, ν = 0.05, γ auto-scaled). Standardized six-dimensional feature vectors captured key environmental and energy-related variables. K-means clustering (k = 3) revealed three persistent operational states: Empty/Cool (42.6%), Experiment (37.6%), and Crowded (19.8%). Detected anomalies included CO₂ surges above 1800 ppm, TVOC concentrations exceeding 4000 ppb, and compound deviations in noise and temperature. The models demonstrated sensitivity to both abrupt and structural anomalies. Latent states were shown to correspond with occupancy patterns, experimental activities, and inactive system operation, offering interpretable environmental profiles. The methodology supports integration into adaptive heating, ventilation, and air conditioning (HVAC) frameworks, enabling real-time, label-free environmental management. Findings contribute to intelligent infrastructure development, particularly in resource-constrained laboratories, and advance progress toward sustainability targets in energy, health, and automation. Full article

The Port of Veracruz, the main port in the Gulf of Mexico, has experienced a significant increase in its import and export operations, such as petroleum coke (Petcoke), a solid waste, mainly used in the steel industry. During the period of 2010–2023, approximately 7,401,594 tons of coke were stored outdoors, generating PM₁₀ particulate emissions due to wind erosion. These particles were dispersed to urban areas, reaching an estimated total emission of 5077 tons. The study used geospatial analysis and environmental modeling tools (ALOHA^®) to evaluate the dispersion and concentration of PM₁₀ in the atmosphere, comparing them with the limits established by the Mexican Official Standard NOM-025-SSA1-2021. The results indicate that in years with high port activity, such as 2014, PM₁₀ concentrations exceeded the normative values, representing a potential risk to public health and urban infrastructure. This study provides critical evidence on the environmental impacts of coke handling in ports and suggests mitigation strategies, including processes for the confinement of materials and the implementation of advanced emissions monitoring systems. Full article

Air pollution has emerged as one of the most critical public health challenges globally, with an astonishing 96% of the world’s population breathing air below the health standards. This study investigates the amount and distribution of six major air pollutants, PM₁₀, PM_2.5, O₃, SO₂, NO₂, and CO, at numerous air monitoring stations across Iran from 2016 to 2021. The primary objectives were to identify the cities with the highest pollution levels, and to assess the spatiotemporal evolution of air pollution across the country, aiming to provide a comprehensive overview and climatology of air quality. The results indicate that cities such as Zabol and Ahvaz consistently rank among the most polluted, with annual average PM₁₀ concentrations exceeding 190 µg m⁻³ and PM_2.5 reaching alarming levels up to 116.7 µg m⁻³. Furthermore, O₃ and SO₂ amounts were high in Zabol too, classifying it as the most polluted city in Iran. In addition, Tehran exhibits high NO₂, SO₂, and CO concentrations due to high industrial activity and vehicular emissions. Seasonal analysis reveals significant variations in pollutant levels, with PM concentrations peaking during specific months over various parts of the country, particularly driven by local and distant dust events. By integrating MERRA-2 reanalysis pollution data and ground measurements, this research provides a robust framework for understanding pollution dynamics, thereby facilitating more effective policy-making and public health interventions. The results underscore the necessity for immediate action to mitigate the adverse effects of air pollution on public health, particularly in areas prone to industrial activities (i.e., Tehran, Isfahan) and dust events (Zabol, Ahvaz). Full article

This study aimed to quantitatively evaluate the potential health effects of exposure to major air pollutants inside newly manufactured automobiles and to develop a grading system for automobile indoor air quality based on this assessment. To achieve this, the concentrations of 28 air pollutants were measured in five different automobile models. Among these, 18 substances were selected for health risk assessment based on the availability of acute and chronic toxicity data and the requirement that each substance had been detected at least once under one or more of the automobile test modes (AM, PM, and DM). Acute hazard quotients (HQ_acute), chronic non-carcinogenic hazard quotients (HQ), and excess lifetime cancer risks (ECR) were subsequently calculated. The results of acute and chronic health risk assessments showed significant variation depending on the automobile test mode, and some automobiles exceeded health-based reference values for certain pollutants. Based on these findings, this study developed a 10-level grading system for automobile indoor air quality by comprehensively integrating pollutant-specific health risk levels and exceedances of the recommended limits outlined in Ministry of Land, Infrastructure, and Transport’s “Indoor Air Quality Guidelines for Newly Manufactured Automobiles.” The grading scale ranges from Grade 1 (Excellent) to Grade 10 (Hazardous), reflecting both acute and chronic health risks as well as legal standards, thereby improving upon conventional concentration-based management approaches. The proposed grading system enables a quantitative interpretation of automobile indoor air quality from a health-based perspective and is expected to be applicable in various fields, including automobile manufacturers’ air quality control, consumer information disclosure, and policy development. Full article