Search Results (1,585)

Air pollutants pose a growing public health concern in Puerto Rico (PR), particularly from rapid industrialization, military activities, environmental changes and natural disasters. A total of 193 pollutants, comprising the 187 hazardous air pollutants and the 6 criteria air pollutants—including particulate matter (PM), carbon monoxide (CO), volatile organic compounds (VOC), and heavy metals—coincide with rising respiratory disease rates (e.g., lung cancer) documented in national and regional health registries. This study aimed to review major air pollutants in PR, their molecular carcinogenic mechanisms (mostly focused on respiratory-related cancers), and the geographic areas impacted significantly. We conducted an extensive literature search utilizing peer-reviewed scientific articles (PubMed and Web of Science), governmental reports (EPA, WHO, State of Global Air), public health registries, (Puerto Rico Central Cancer Registry and International Agency for Research on Cancer) and local reports. Data on pollutant type, source, molecular pathways, and carcinogenic properties were extracted and synthesized. Our analysis identified ethylene oxide (EtO), polycyclic aromatic hydrocarbons, and PM from industrial sites as key pollutants. The municipalities of Salinas and Vieques, hubs of industrial activity and military exercises, respectively, emerged as critical hotspots where high concentrations of monitored pollutants (e.g., EtO, formaldehyde, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and diesel PM) are associated with a significant prevalence of cancer and respiratory diseases. These agents, known to induce genomic instability and chromosomal aberrations, were correlated with elevated local cancer incidence. Our findings underscore the urgent need for targeted public health interventions and support a multi-pronged strategy that includes: (1) enhanced regulatory oversight of EtO and other hazardous air pollutant emissions; (2) community-based biomonitoring of high-risk populations; and (3) investment in public health infrastructure and a transition to cleaner energy sources. Integrating rigorous environmental science with public health advocacy is essential to strengthen PR’s cancer-control continuum and foster resilience in its most vulnerable communities. Full article

White mud is a promising desulfurizing agent, but the risk of fine particulate emissions exists during its application. This study investigated the fine particulate emissions in the white mud desulfurization process and analyzed the effects of process parameters, including gas-to-liquid ratio, empty tower gas velocity, and slurry concentration, on particulate emissions. The results showed that white mud desulfurization achieved effective SO₂ removal, with a removal efficiency ranging from 93.5% to 95.8%. However, the emission of fine particulates was found to be a significant environmental concern. At a slurry concentration of 15%, the fine particulate number concentration was found to be 5.9 × 10⁶ particles/cm³, with a mass concentration of approximately 43.2 mg/m³. The study further revealed that increasing the empty tower gas velocity from 2.5 m/s to 4.5 m/s also significantly increased particulate emissions. Similarly, increasing the gas-to-liquid ratio from 10 L/m³ to 15 L/m³ led to a 25.5% increase in the fine particulate number concentration. These changes were attributed to the increased atomization of fine droplets and the enhanced gas–liquid relative movement, which facilitated the entrainment of more fine particulates into the flue gas. While improving the slurry concentration led to better desulfurization efficiency, these adjustments also resulted in higher fine particulate emissions. Therefore, optimizing process parameters to balance desulfurization efficiency and fine particulate emission control was crucial for practical applications. Full article

Particulate matter (PM) concentration, especially PM_2.5, is a major culprit of environmental pollution from unreasonable energy system emissions that significantly affects visibility, climate, and public health. The prediction of PM_2.5 concentration holds significant importance in the early warning and management of severe air pollution, since it enables the provision of guidance for scientific decision-making through the estimation of impending PM_2.5 concentration. However, due to diversified human activities, seasonal factors and industrial emissions, the air quality data not only show local anomalous mutability, but also global dynamic change characteristics. This hinders existing PM_2.5 prediction models from fully capturing the aforementioned characteristics, thereby deteriorating the model performance. To address these issues, this study proposes a framework integrating multi-scale temporal convolutional networks (TCNs) and a transformer network (called MSTTNet) for PM_2.5 concentration prediction. Specifically, MSTTNet uses multi-scale TCNs to capture the local correlations of meteorological and pollutant data in a fine-grained manner, while using transformers to capture the global temporal relationships. The proposed MSTTNet’s performance has been validated on various air quality benchmark datasets in the cities of China, including Beijing, Shanghai, Chengdu, and Guangzhou, by comparing to its eight compared models. Comprehensive experiments confirm that the MSTTNet model can improve the prediction performance of 2.42%, 2.17%, 2.87%, and 0.34%, respectively, with respect to four evaluation indicators (i.e., Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, and R-square), relative to the optimal baseline model. These results confirm MSTTNet’s effectiveness in improving the accuracy of PM_2.5 concentration prediction. Full article

Cyprus is located between Europe, Asia and Africa, and its location is vulnerable to dust transport from the Sahara Desert, wildfire smoke particles from surrounding regions, and other anthropogenic emissions caused by several factors, mostly due to business activities on harbor areas. Moreover, the country suffers from heavy traffic conditions caused by the limited public transportation system in Cyprus. Therefore, taking into consideration the country’s geographic location, heavy commercial activities, and lack of good public transportation system, Cyprus is exposed to dust episodes and high anthropogenic emissions associated with multiple health and environmental issues. Therefore, continuous and qualitative air quality monitoring is essential. The Department of Labor Inspection of Cyprus (DLI) has established an air quality monitoring network that consists of 11 stations at strategic geographic locations covering rural, residential, traffic and industrial zones. This network measures the following pollutants: nitrogen oxide, nitrogen dioxide, sulfur dioxide, ozone, carbon monoxide, particulate matter 2.5, and particulate matter 10. This case study compares and evaluates the agreement between Copernicus Atmosphere Monitoring Service (CAMS) air quality products and ground-truth data from the DLI air quality network. The study period spans from January to December 2024. This study focuses on the following three pollutants: particulate matter 2.5, particulate matter 10, and ozone, using Ensemble Median, EMEP, and CHIMERE near-real-time model data provided by CAMS. A data analysis was performed to identify the agreement and the error rate between those two datasets (i.e., ground-truth air quality data and CAMS air quality data). In addition, this study assesses the reliability of assimilated datasets from CAMS across rural, residential, traffic and industrial zones. The results showcase how CAMS near-real-time analysis data can supplement air quality monitoring in locations without the availability of ground-truth data. Full article

Selecting an appropriate Multi-Criteria Decision-Making (MCDM) algorithm for optimizing marine diesel engine operation presents a complex challenge due to the diversity in mathematical formulations, normalization schemes, and trade-off resolutions across methods. This study systematically evaluates fourteen MCDM algorithms, which are grouped into five primary methodological categories: Scoring-Based, Distance-Based, Pairwise Comparison, Outranking, and Hybrid/Intelligent System-Based methods. The goal is to identify the most suitable algorithm for real-time performance optimization of two stroke marine diesel engines. Using Diesel-RK software, calibrated for marine diesel applications, simulations were performed on a variant of the MAN-B&W-S60-MC-C8-8 engine. A refined five-dimensional parameter space was constructed by systematically varying five key control variables: Start of Injection (SOI), Dwell Time, Fuel Mass Fraction, Fuel Rail Pressure, and Exhaust Valve Timing. A subset of 4454 high-potential alternatives was systematically evaluated according to three equally important criteria: Specific Fuel Consumption (SFC), Nitrogen Oxides (NO_x), and Particulate Matter (PM). The MCDM algorithms were evaluated based on ranking consistency and stability. Among them, Proximity Indexed Value (PIV), Integrated Simple Weighted Sum Product (WISP), and TriMetric Fusion (TMF) emerged as the most stable and consistently aligned with the overall consensus. These methods reliably identified optimal engine control strategies with minimal sensitivity to normalization, making them the most suitable candidates for integration into automated marine engine decision-support systems. The results underscore the importance of algorithm selection and provide a rigorous basis for establishing MCDM in emission-constrained maritime environments. This study is the first comprehensive, simulation-based evaluation of fourteen MCDM algorithms applied specifically to the optimization of two stroke marine diesel engines using Diesel-RK software. Full article

Air quality is a vital factor for safeguarding public and environmental health. Particulate matter (i.e., PM2.5 and PM10) and nitrogen dioxide are among the most harmful air pollutants leading to severe health risks such as respiratory and cardiovascular diseases, while also affecting the environment negatively by contributing to the formation of acid rains and ground level ozone. The European Union has introduced new thresholds on those pollutants to be met by the year 2030, taking into consideration the guidelines set by the World Health Organization, aiming for a healthier environment for humans and living species. Cyprus is an island that is vulnerable to those pollutants mostly due to its geographic location, facilitating shipping activities and dust transport from Sahara Desert, and the methods used to produce electricity which primarily rely on petroleum products. Furthermore, the country suffers from heavy traffic conditions, making it susceptible to high levels of nitrogen dioxide. Thus, the projection of air pollutants according to different scenarios based on regulations and policies of the European Union are necessary towards clean air and better practices. The Screening for High Emission Reduction Potential on Air (SHERPA) is a tool developed by the European Commission which allows the simulation of emission reduction scenarios and their effect on the following key pollutants: NO, NO₂, O₃, PM2.5, PM10. This study aims to assess the potential of the SHERPA simulation tool to support air quality related decision and policy planning in Cyprus to ensure that the country will remain within the thresholds that will be applicable in 2030. Full article

Urban industrial complexes have been expanding worldwide, reducing the spatial separation between agricultural, residential, and industrial zones, particularly in developing nations. Urban road dust contamination, a sensitive indicator of urban environmental quality, primarily originates in urbanization and industrialization. Its detrimental impacts on human health arise not only from particulate matter itself but also from toxic and harmful substances embedded within dust particles. Toxic metals in road dust can pose health risks through inhalation, ingestion and contact. To investigate the seasonal patterns, bioaccessibility levels and the potential human health risks linked to toxic metals (Cadmium (Cd), Nickel (Ni), Arsenic (As), Lead (Pb), Zinc (Zn), Copper (Cu), and Chromium (Cr)), 34 dust samples were collected from key roads in proximity to representative industrial facilities in Wuhan’s Qingshan District. The study found that the concentrations of Cd, Pb, and Cu in road dust were within the limits set by the national standard (GB 15618-2018), while Ni and As were not. Seasonally, Ni, As, Pb, Zn, and Cr exhibited higher concentrations during the summer than in other seasons, whereas Cd levels were lowest in spring and highest in autumn, the opposite of Cu. According to the Simplified Bioaccessibility Extraction Test (SBET), the average bioaccessibility rates of toxic metals were Cd > Zn > Cu > Ni > Cr > As > Pb. An improved health risk assessment model was developed, integrating metal enrichment, bioaccessibility, and parameter uncertainty. Results indicated that Cd, Ni, Zn, Cu, As, and Cr posed no significant non-carcinogenic risk. However, for children, the carcinogenic risks of Cd and As were relatively high, identifying them as priority control metals. Therefore, it is recommended to periodically monitor As and Cd and regulate their potential emission sources, especially in winter and spring. Full article

Air quality is crucial for public health and the environment, which makes it important to both monitor and forecast the level of pollution. Polluted air, containing harmful substances such as particulate matter, nitrogen oxides, or ozone, can lead to serious respiratory and circulatory diseases, especially in people at risk. Air quality forecasting allows for early warning of smog episodes and taking actions to reduce pollutant emissions. In this article, we review air pollutant concentration forecasting methods, analyzing both classical statistical approaches and modern techniques based on artificial intelligence, including deep models, neural networks, and machine learning, as well as advanced sensing technologies. This work aims to present the current state of research and identify the most promising directions of development in air quality modeling, which can contribute to more effective health and environmental protection. According to the reviewed literature, deep learning–based models, particularly hybrid and attention-driven architectures, emerge as the most promising approaches, while persistent challenges such as data quality, interpretability, and integration of heterogeneous sensing systems define the open issues for future research. Full article

NO₂ is a toxic gas mainly generated by combustion processes, such as vehicle emissions and industrial activities. It is a key contributor to smog, acid rain, ground-level ozone, and particulate matter, all of which pose serious risks to human health and the environment. Conventional resistive gas sensors, typically based on metal oxide semiconductors, detect NO₂ by resistance modulation through surface interactions with the gas. However, they often suffer from low responsiveness and poor selectivity. This study investigates NO₂ detection using nanoporous zinc oxide thin films integrated into a resistor structure and floating-gate field-effect transistor (FGFET). Both Silvaco-Atlas simulations and experimental fabrication were employed to evaluate sensor behavior under NO₂ exposure. The results show that FGFET provides higher sensitivity, faster response times, and improved selectivity compared to resistor-based devices. In particular, FGFET achieves a detection limit as low as 89 ppb, with optimal performance around 400 °C, and maintains stability under varying humidity levels. The enhanced performance arises from quantum well effects at the floating-gate Schottky contact, combined with NO₂ adsorption on the ZnO surface. These interactions extend the depletion region and confine charge carriers, amplifying conductivity modulation in the channel. Overall, the findings demonstrate that FGFET is a promising platform for NO₂ sensors, with strong potential for environmental monitoring and industrial safety applications. Full article

Bulgaria continues to face serious challenges related to air quality. To mitigate traffic-related air pollution and in line with the European regulations, the Metropolitan Municipal Council of Sofia has adopted and introduced low-emission zones (LEZs) in the city centre. The goal of this study is to address the specific needs of urban planning in the city in support of local decision-making. A bespoke emission inventory was developed for the LEZs in Sofia, and high-resolution numerical simulations (100 m resolution) were carried out to assess the effect of the measures implemented to reduce emissions in the central part of the city. The results show a decrease in nitrogen dioxide concentrations along major roads and intersections, but projected concentrations will still be high. No significant improvement is expected for particulate matter pollution due to the limitations of this study. High-resolution (100 m) emission inventories of domestic heating, minor roads, and bare soil surfaces, the major sources of particulate matter pollution, are not included in this study. An integrated model is needed to analyse and compare different scenarios for the development of the transport system, and the gradual introduction of LEZs must be accompanied by a number of other additional measures and actions. Full article

As many countries transition to electric vehicles (EVs) to reduce tailpipe emissions from internal combustion engine vehicles (ICEVs), both vehicle types continue to generate non-exhaust particulate matter (PM), including tire wear, brake wear, road surface wear, and particularly road dust resuspension. Among these, road dust resuspension is a major contributor to non-exhaust PM. While factors such as vehicle weight and drivetrain configuration have been extensively studied in fleet-level research, direct comparisons between ICEVs and EVs of the same model have not been explored. This study investigates the effects of drivetrain, vehicle weight, and payload on road dust resuspension emissions from ICEV and EV models. Two experimental approaches were employed: (1) acceleration from 0 to 60 km/h, and (2) a simulated real-world driving cycle (RDC). Each test was conducted under both light and heavy payload conditions. The results show that the EV consistently emitted more PM than the ICEV during both acceleration and RDC tests, based on factory-standard vehicle weights. Under identical vehicle weight conditions, the EV demonstrated higher PM resuspension levels, likely due to its higher torque and more immediate power delivery, which increases friction between the tires and the road, particularly during rapid acceleration. Both vehicle types exhibited significant increases in PM emissions under heavy payload conditions. These findings underscore the importance of addressing non-exhaust emissions from EVs, particularly road dust resuspension, and highlight the need for further research into mitigation strategies, such as vehicle lightweighting. Full article

Three TOP TIER^TM gasoline deposit control additives (DCAs) of differing chemistries were tested for their impact on particulate matter emissions in terms of particulate mass (PM) and particle number (PN) at operating conditions representative of road load, cold start, and high load on a 2.0 L, 4-cylinder, gasoline direct injection (GDI) spark ignition (SI) engine. The PM-PN emissions were measured using an Exhaust Emissions Particle Sizer (EEPS). Deposit control additives or detergents are gasoline additives used to prevent and clean combustion chamber and injector deposits in gasoline spark ignition (SI) engines. All three gasoline additives were tested at each operating condition at three different treatment rates. In addition, one of the additives was tested with a fuel-based friction modifier (FM). The results showed that of the treatment rates tested, the lowest allowable concentration (LAC) for all additives requires the least time for the emissions to settle. However, the impact of the gasoline additives on PM-PN emissions is not linear and changes with additive concentration depending on the additive chemistry and operating conditions. The additive with the friction modifier resulted in an increase of over 19% particle number and over 30% particulate mass at the road load operating condition, while the increase at high load was over 27% for particle number and 11% for particle mass. Full article

Indoor air pollution has become a significant concern, contributing to the decline in air quality through the presence of gaseous pollutants and particulate matter, especially under poor ventilation. Hospitals, functioning as non-industrial microenvironments, particularly in Thailand, face challenges due to insufficient and incomplete databases for effective air quality management. Within these environments, patients with heightened sensitivity, along with hospital staff who are predominantly exposed indoors, face increased risk of exposure to indoor air pollutants. This study aimed to review current evidence on VOCs in hospital settings in Thailand, identifying their sources, concentrations, and health impacts. It also aimed to provide recommendations for improved air quality monitoring and management. The review included studies published between 2008 and 2023 in English or Thai. Studies were selected based on relevance to VOCs in hospital environments, while excluding those lacking sufficient data or methodological rigor. Literature searches were conducted using Google Scholar, ScienceDirect, Scopus, and PubMed. Results from international studies were also considered to address gaps. Data extraction focused on VOC sources, concentrations, measurement methods, and associated health impacts. Results were synthesized into six thematic categories: characterization, health effects, control measures, etiological studies, monitoring systems, and comparative studies. The review identified 87 relevant studies. VOC exposure was associated with several adverse health impacts resulting from short- and long-term exposures, leading to an increased risk of cancer. Identified sources of VOC emissions within hospitals encompass anesthetic gases, sterilization processes, pharmaceuticals, laboratory chemicals, patient care, and household products, as well as building materials and furnishings. Commonly encountered VOCs include alcohols (e.g., ethanol, 2-methyl-2-propanol, isopropanol), ether, isoflurane, nitrous oxide, sevoflurane, chlorine, formaldehyde, aromatic hydrocarbons, limonene, and glutaraldehyde, among those commonly detected in hospital environments. Yet, limited knowledge exists regarding their source contributions, emissions, and concentrations associated with health impacts in Thai hospitals. Full article

The National Cookstove Program has been launched by the Federal Government of Mexico, attempting to reach one million rural homes by the year 2030. Voluntary ISO emission standards for fine particulate matter (PM_2.5) and carbon monoxide (CO) relate emission rates from stoves to indoor air concentrations using a single zone box model (SZM) to derive performance tiers. Region-specific emission benchmarks for cookstove performance that are linked to estimated benefits in reduced indoor air concentrations and resultant health impacts will be important in product selection. Here we compare the SZM to measured indoor PM_2.5 and CO concentrations for five stove stacking combinations using controlled cooking tests of typical foods from the Purepecha region of Mexico to derive region-specific benchmarks. The results demonstrate that the SZM systematically overpredicted PM_2.5 emissions based on thermal plume effects and ventilation which can be adjusted based on strong relationships (Adjusted r² = 0.96, p < 0.001) with emission rates and air changes per hour. Adjustment of PM_2.5 ISO voluntary standards for systematic bias caused by plume buoyancy and ventilation is important in ensuring that the ISO benchmarks reflect the actual indoor concentrations measured in homes. The ISO benchmarks for CO should be revisited as the indoor concentrations from traditional stoves met the most stringent benchmarks but were in the range of concentrations associated with adverse health impacts in adults and psychosocial impacts in children. 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