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Search Results (553)

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Keywords = pollutant emissions prediction

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19 pages, 440 KiB  
Article
Cost-Benefit Analysis of Diesel vs. Electric Buses in Low-Density Areas: A Case Study City of Jastrebarsko
by Marko Šoštarić, Marijan Jakovljević, Marko Švajda and Juraj Leonard Vertlberg
World Electr. Veh. J. 2025, 16(8), 431; https://doi.org/10.3390/wevj16080431 (registering DOI) - 1 Aug 2025
Abstract
This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle [...] Read more.
This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article
(This article belongs to the Special Issue Zero Emission Buses for Public Transport)
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19 pages, 13565 KiB  
Article
Estimation of Ultrahigh Resolution PM2.5 in Urban Areas by Using 30 m Landsat-8 and Sentinel-2 AOD Retrievals
by Hao Lin, Siwei Li, Jiqiang Niu, Jie Yang, Qingxin Wang, Wenqiao Li and Shengpeng Liu
Remote Sens. 2025, 17(15), 2609; https://doi.org/10.3390/rs17152609 - 27 Jul 2025
Viewed by 205
Abstract
Ultrahigh resolution fine particulate matter (PM2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate [...] Read more.
Ultrahigh resolution fine particulate matter (PM2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R2) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R2 values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article
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30 pages, 9606 KiB  
Article
A Visualized Analysis of Research Hotspots and Trends on the Ecological Impact of Volatile Organic Compounds
by Xuxu Guo, Qiurong Lei, Xingzhou Li, Jing Chen and Chuanjian Yi
Atmosphere 2025, 16(8), 900; https://doi.org/10.3390/atmos16080900 - 24 Jul 2025
Viewed by 349
Abstract
With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and [...] Read more.
With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article
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22 pages, 1534 KiB  
Article
Predictability of Air Pollutants Based on Detrended Fluctuation Analysis: Ekibastuz Сoal-Mining Center in Northeastern Kazakhstan
by Oleksandr Kuchanskyi, Andrii Biloshchytskyi, Yurii Andrashko, Alexandr Neftissov, Svitlana Biloshchytska and Sergiy Bronin
Urban Sci. 2025, 9(7), 273; https://doi.org/10.3390/urbansci9070273 - 16 Jul 2025
Viewed by 527
Abstract
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 [...] Read more.
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, PM2.5, and PM10, as well as current mass consumption of CO, NO, NO2, SO2, dust, and NOx. 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
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30 pages, 1095 KiB  
Article
Unraveling the Drivers of ESG Performance in Chinese Firms: An Explainable Machine-Learning Approach
by Hyojin Kim and Myounggu Lee
Systems 2025, 13(7), 578; https://doi.org/10.3390/systems13070578 - 14 Jul 2025
Viewed by 399
Abstract
As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders [...] Read more.
As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article
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35 pages, 6888 KiB  
Article
AirTrace-SA: Air Pollution Tracing for Source Attribution
by Wenchuan Zhao, Qi Zhang, Ting Shu and Xia Du
Information 2025, 16(7), 603; https://doi.org/10.3390/info16070603 - 13 Jul 2025
Viewed by 278
Abstract
Air pollution source tracing is vital for effective pollution prevention and control, yet traditional methods often require large amounts of manual data, have limited cross-regional generalizability, and present challenges in capturing complex pollutant interactions. This study introduces AirTrace-SA (Air Pollution Tracing for Source [...] Read more.
Air pollution source tracing is vital for effective pollution prevention and control, yet traditional methods often require large amounts of manual data, have limited cross-regional generalizability, and present challenges in capturing complex pollutant interactions. This study introduces AirTrace-SA (Air Pollution Tracing for Source Attribution), a novel hybrid deep learning model designed for the accurate identification and quantification of air pollution sources. AirTrace-SA comprises three main components: a hierarchical feature extractor (HFE) that extracts multi-scale features from chemical components, a source association bridge (SAB) that links chemical features to pollution sources through a multi-step decision mechanism, and a source contribution quantifier (SCQ) based on the TabNet regressor for the precise prediction of source contributions. Evaluated on real air quality datasets from five cities (Lanzhou, Luoyang, Haikou, Urumqi, and Hangzhou), AirTrace-SA achieves an average R2 of 0.88 (ranging from 0.84 to 0.94 across 10-fold cross-validation), an average mean absolute error (MAE) of 0.60 (ranging from 0.46 to 0.78 across five cities), and an average root mean square error (RMSE) of 1.06 (ranging from 0.51 to 1.62 across ten pollution sources). The model outperforms baseline models such as 1D CNN and LightGBM in terms of stability, accuracy, and cross-city generalization. Feature importance analysis identifies the main contributions of source categories, further improving interpretability. By reducing the reliance on labor-intensive data collection and providing scalable, high-precision source tracing, AirTrace-SA offers a powerful tool for environmental management that supports targeted emission reduction strategies and sustainable development. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining: Innovations in Big Data Analytics)
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30 pages, 5800 KiB  
Article
Mitigating Environmental Impact Through the Use of Rice Husk Ash in Sustainable Concrete: Experimental Study, Numerical Modelling, and Optimisation
by Md Jihad Miah, Mohammad Shamim Miah, Humera Mughal and Noor Md. Sadiqul Hasan
Materials 2025, 18(14), 3298; https://doi.org/10.3390/ma18143298 - 13 Jul 2025
Cited by 1 | Viewed by 532
Abstract
Cement production significantly contributes to CO2 emissions (8% of worldwide CO2 emissions) and global warming, accelerating climate change and increasing air pollution, which harms ecosystems and human health. To this end, this research investigates the fresh and hardened properties of sustainable [...] Read more.
Cement production significantly contributes to CO2 emissions (8% of worldwide CO2 emissions) and global warming, accelerating climate change and increasing air pollution, which harms ecosystems and human health. To this end, this research investigates the fresh and hardened properties of sustainable concrete fabricated with three different replacement percentages (0%, 5%, and 10% by weight) of ordinary Portland cement (OPC) using rice husk ash (RHA). The hardened properties were evaluated at 14, 28, 60, 90, and 120 days of water curing. In addition, data-based models were developed, validated, and optimised, and the models were compared with experimental results and validated with the literature findings. The outcomes reveal that the slump values increased (17% higher) with the increased content of RHA, which aligns with the lower temperatures (12% lower) of freshly mixed concrete with RHA than the control mix (100% OPC). The slopes of the stress–strain profiles decreased at early ages and improved at longer curing ages (more than 28 days), especially for mixes with 5% RHA. The compressive strength decreased slightly (18% at 28 days) with increased percentages of RHA, which was minimised with increased curing ages (8% at 90 days). The data-based model accurately predicted the stress–strain profiles (coefficient of determination, R2 ≈ 0.9950–0.9993) and compressive strength at each curing age, including crack progression (i.e., highly nonlinear region) and validates its effectiveness. In contrast, the optimisation model shows excellent results, mirroring the experimental data throughout the profile. These outcomes indicate that the 10% RHA could potentially replace OPC due to its lower reduction in strength (8% at 90 days), which in turn lowers CO2 emissions and promotes sustainability. Full article
(This article belongs to the Special Issue Sustainability and Performance of Cement-Based Materials)
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22 pages, 2101 KiB  
Article
Forecast of CO2 and Pollutant Emission Reductions from Electric Vehicles in Beijing–Tianjin–Hebei
by Li Li, Honglin Liu and Bingchun Liu
Sustainability 2025, 17(14), 6386; https://doi.org/10.3390/su17146386 - 11 Jul 2025
Viewed by 287
Abstract
The promotion of new energy vehicles (NEVs) represents a critical strategy for mitigating carbon emissions and air pollution. To evaluate the CO2 and air pollutant reduction potential of NEVs in the Beijing–Tianjin–Hebei region, this study developed an integrated framework combining gray correlation [...] Read more.
The promotion of new energy vehicles (NEVs) represents a critical strategy for mitigating carbon emissions and air pollution. To evaluate the CO2 and air pollutant reduction potential of NEVs in the Beijing–Tianjin–Hebei region, this study developed an integrated framework combining gray correlation analysis (GRA) and bidirectional long short-term memory (BiLSTM), referred to as the GRA-BiLSTM model, to forecast the adoption trend of NEVs and calculate the CO2 and air pollutant emission reduction. The GRA-BiLSTM model developed in this study shows optimal predictive performance. The results indicate that new energy vehicles (NEVs) have great potential for environmental collaborative emission reduction in the transportation sector: it is predicted that by 2035, the total number of NEVs will be nearly 11.88 million, with a cumulative reduction of 2.76 billion tons of carbon emissions and significant reductions in various key air pollutants. This study provides an important quantitative basis for formulating pollution reduction and carbon reduction policies in the transportation sector. Full article
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35 pages, 1595 KiB  
Article
Analysis of the Synergies of Air Pollutant and Greenhouse Gas Emission Reduction in Typical Chemical Enterprises
by Qi Gong, Yatfei Chan, Yijia Xia, Weiqi Tang and Weichun Ma
Sustainability 2025, 17(14), 6263; https://doi.org/10.3390/su17146263 - 8 Jul 2025
Viewed by 277
Abstract
In this study, we selected the production processes and main products of three typical chemical enterprises in Shanghai, namely SH Petrochemical (part of the oil-refining sector), SK Ethylene, and HS Chlor-Alkali, to quantitatively assess the synergistic effects across technology, policy, and emission mechanisms. [...] Read more.
In this study, we selected the production processes and main products of three typical chemical enterprises in Shanghai, namely SH Petrochemical (part of the oil-refining sector), SK Ethylene, and HS Chlor-Alkali, to quantitatively assess the synergistic effects across technology, policy, and emission mechanisms. The localized air pollutant levels and greenhouse gas emissions of the three enterprises were calculated. The synergistic effects between the end-of-pipe emission reductions for air pollutants and greenhouse gas emissions were analyzed using the pollutant reduction synergistic and cross-elasticity coefficients, including technology comparisons (e.g., acrylonitrile gas incineration (AOGI) technology vs. traditional flare). Based on these data, we used the SimaPro software and the CML-IA model to conduct a life cycle environmental impact assessment regarding the production and upstream processes of their unit products. By combining the life cycle method and the scenario simulation method, we predicted the trends in the environmental impacts of the three chemical enterprises after the implementation of low-carbon development policies in the chemical industry in 2030. We also quantified the synergistic effects of localized air pollutant and greenhouse gas (GHG) emission reductions within the low-carbon development scenario by using cross-elasticity coefficients based on life cycle environmental impacts. The research results show that, for every ton of air pollutant reduced through end-of-pipe treatment measures, the HS Chlor-Alkali enterprise would increase its maximum CO2 emissions, amounting to about 80 tons. For SK Ethylene, the synergistic coefficient for VOC reduction and CO2 emissions when using AOGI thermal incineration technology is superior to that for traditional flare thermal incineration. The activities of the three enterprises had an impact on several environmental indicators, particularly the fossil fuel resource depletion potential, accounting for 69.48%, 53.94%, and 34.23% of their total environmental impact loads, respectively. The scenario simulations indicate that, in a low-carbon development scenario, the overall environmental impact loads of SH Petrochemical (refining sector), SK Ethylene, and HS Chlor-Alkali would decrease by 3~5%. This result suggests that optimizing the upstream power structure, using “green hydrogen” instead of “grey hydrogen” in hydrogenation units within refining enterprises, and reducing the consumption of electricity and steam in the production processes of ethylene and chlor-alkali are effective measures in reducing carbon emissions in the chemical industry. The quantification of the synergies based on life cycle environmental impacts revealed that there are relatively strong synergies for air pollutant and GHG emission reductions in the oil-refining industry, while the chlor-alkali industry has the weakest synergies. Full article
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34 pages, 2400 KiB  
Review
Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej and Grzegorz Wilk-Jakubowski
Appl. Sci. 2025, 15(13), 7204; https://doi.org/10.3390/app15137204 - 26 Jun 2025
Viewed by 495
Abstract
This review article provides a comprehensive analysis of the recent advancements in combustion science and engineering, focusing on the application of machine learning and genetic algorithms from 2015 to 2024. The study examines the integration of computational methods, including computational fluid dynamics, neural [...] Read more.
This review article provides a comprehensive analysis of the recent advancements in combustion science and engineering, focusing on the application of machine learning and genetic algorithms from 2015 to 2024. The study examines the integration of computational methods, including computational fluid dynamics, neural networks, and genetic algorithms, with various fuel types such as biodiesel, biomass, coal, gasoline, hydrogen, and natural gas. A systematic search in the Scopus database identified relevant articles, which were categorized based on fuel types and computational methodologies. The analysis covers key areas such as combustion modelling and simulation, engine applications, alternative fuels, pollutant control, and industrial combustion systems. This review highlights the growing role of machine learning and genetic algorithms in enhancing combustion efficiency, reducing emissions, and optimizing energy production, providing insights into the current state of the art and future trends in this critical field. The study further examines the geographical distribution of research, noting significant contributions from Canada, China, France, Germany, India, Iran, Japan, Malaysia, Pakistan, Saudi Arabia, the United Kingdom, and the United States, alongside other international contributions. A total of 165 peer-reviewed articles were analyzed, covering a range of combustion scenarios and fuel types. The most frequently applied methods include artificial neural networks (ANNs), support vector machines (SVMs), and random forests (RFs) for predictive modeling, as well as genetic algorithms (GAs) for system optimization. ANN-based models achieved high accuracy in predicting NOx emissions and flame speed, with some studies reporting mean absolute errors below 5%. GA methods demonstrated effectiveness in fuel blend optimization and geometry design, achieving emission reductions of up to 30% in experimental setups. This review also highlights persistent challenges such as data availability, model generalization, and reproducibility, and proposes future directions toward more interpretable and standardized applications of ML/GA in combustion science. Full article
(This article belongs to the Special Issue Advances in Combustion Science and Engineering)
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25 pages, 4660 KiB  
Article
CO Emission Prediction Based on Kernel Feature Space Semi-Supervised Concept Drift Detection in Municipal Solid Waste Incineration Process
by Runyu Zhang, Jian Tang and Tianzheng Wang
Sustainability 2025, 17(13), 5672; https://doi.org/10.3390/su17135672 - 20 Jun 2025
Viewed by 310
Abstract
Carbon monoxide (CO) is a toxic pollutant emitted by municipal solid waste incineration (MSWI), which has a strong correlation with dioxins. In terms of the sustainable development of an ecological environment, CO emission concentration is strictly controlled by the environmental departments of various [...] Read more.
Carbon monoxide (CO) is a toxic pollutant emitted by municipal solid waste incineration (MSWI), which has a strong correlation with dioxins. In terms of the sustainable development of an ecological environment, CO emission concentration is strictly controlled by the environmental departments of various countries in the world. The construction of its prediction model is conducive to pollution reduction control. The MSWI process is affected by multi-factors such as MSW component fluctuation, equipment wear and maintenance, and seasonal change, and has complex nonlinear and time-varying characteristics, which makes it difficult for the CO prediction model based on offline historical data to adapt to the above changes. In addition, the continuous emission monitoring system (CEMS) used for conventional pollutant detection has unavoidable misalignment and failure problems. In this article, a novel prediction model of CO emission from the MSWI process based on semi-supervised concept drift (CD) detection in kernel feature space is proposed. Firstly, the CO emission deep prediction model and the kernel feature space detection model are constructed based on offline batched historical data, and the historical data set for the real-time construction of the pseudo-labeling model is obtained. Secondly, the drift detection for the CO emission prediction model is carried out based on real-time data by using unsupervised kernel principal component analysis (KPCA) in terms of feature space. If CD occurs, the pseudo-label model is constructed, the pseudo-truth value is obtained, and the drift sample is confirmed and selected based on the Page–Hinkley (PH) test. If no CD occurs, the CO emission concentration is predicted based on the historical prediction model. Then, the updated data set of the CO emission prediction model and kernel feature space detection is obtained by combining historical samples and drift samples. Finally, the offline history model is updated with a new data set when the preset conditions are met. Based on the real data set of an MSWI power plant in Beijing, the validity of the proposed method is verified. Full article
(This article belongs to the Special Issue Novel and Scalable Technologies for Sustainable Waste Management)
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19 pages, 9490 KiB  
Article
Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method
by Xinyu Zou, Xinlong Li, Dali Wang and Ju Wang
Toxics 2025, 13(6), 500; https://doi.org/10.3390/toxics13060500 - 13 Jun 2025
Viewed by 634
Abstract
Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O3) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed [...] Read more.
Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O3) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O3 concentrations. Finally, the HYSPLIT clustering method and CMAQ model are utilized to analyze inter-regional transport characteristics, identifying the causes of O3 pollution. The results indicate that O3 pollution in Liaoyuan exhibits a distinct seasonal pattern, with the highest concentrations found in spring and summer, peaking in the afternoon. Among the three ML models, the random forest model demonstrates the best predictive performance (R2 = 0.9043). Feature importance identifies NO2 as the primary driving factor, followed by meteorological conditions in the second quarter and land surface characteristics. Furthermore, regional transport significantly contributes to O3 pollution, with approximately 80% of air mass trajectories in heavily polluted episodes originating from adjacent industrial areas and the sea. The combined effects of transboundary precursors and O3 transport with local emissions and meteorological conditions further increase the O3 pollution level. This study highlights the need to strengthen coordinated NOX and VOCs emission reductions and enhance regional joint prevention and control strategies in China. Full article
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30 pages, 3202 KiB  
Article
A Comprehensive Model for Quantifying, Predicting, and Evaluating Ship Emissions in Port Areas Using Novel Metrics and Machine Learning Methods
by Filip Bojić, Anita Gudelj and Rino Bošnjak
J. Mar. Sci. Eng. 2025, 13(6), 1162; https://doi.org/10.3390/jmse13061162 - 12 Jun 2025
Viewed by 448
Abstract
Seaports, as major transportation hubs, generate significant air pollution due to intensive ship traffic, directly affecting local air quality. While emission inventories are commonly used to manage ship-based air pollution, they reflect only the emission-related aspect of a specified period and area, limiting [...] Read more.
Seaports, as major transportation hubs, generate significant air pollution due to intensive ship traffic, directly affecting local air quality. While emission inventories are commonly used to manage ship-based air pollution, they reflect only the emission-related aspect of a specified period and area, limiting the broader interpretability and comparability of the results. To overcome the mentioned challenges, this research presents the PrE-PARE model, which enables the prediction, analysis, and risk evaluation of ship-sourced air pollution in port areas. The model comprises three interconnected modules. The first is applied for quantifying emissions using detailed technical and movement datasets, which are combined into individual voyage trajectories to enable a high-resolution analysis of ship-based air pollutants. In the second module, the Multivariate Adaptive Regression Splines (MARS) machine learning method is adapted to predict emissions in varying operational scenarios. In the third module, novel metric methods are introduced, enabling a standardised efficiency comparison between ships. These methods are supported by a unique classification system to determine the emission risk in different periods, evaluate the intensity of various ship types, and rank individual ships based on their operational efficiency and emission optimisation potential. By combining new methods with technical and operational shipping data, the model provides a transparent, comparable, and adaptable system for emissions monitoring. The results demonstrate that the PrE-PARE model has the potential to improve strategic planning and air quality management in ports while remaining flexible enough to be applied in different contexts and future scenarios. Full article
(This article belongs to the Special Issue Sustainable Maritime Transport and Port Intelligence)
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17 pages, 1128 KiB  
Article
Forecasting Air Pollutant Emissions Using Deep Sparse Transformer Networks: A Case Study of the Ekibastuz Coal-Fired Power Plant
by Yurii Andrashko, Oleksandr Kuchanskyi, Andrii Biloshchytskyi, Alexandr Neftissov and Svitlana Biloshchytska
Sustainability 2025, 17(11), 5115; https://doi.org/10.3390/su17115115 - 3 Jun 2025
Cited by 1 | Viewed by 578
Abstract
It is important to predict air pollutant emissions from coal-fired power plants using real-time technological parameters to improve environmental efficiency. Since the relationship between emissions and parameters is nonlinear, machine learning models are needed to forecast emissions under various boiler operating modes. This [...] Read more.
It is important to predict air pollutant emissions from coal-fired power plants using real-time technological parameters to improve environmental efficiency. Since the relationship between emissions and parameters is nonlinear, machine learning models are needed to forecast emissions under various boiler operating modes. This study develops and tests Deep Sparse Transformer Networks for predicting pollutant time series, accounting for long-term dependencies. Data were collected from a 4000 MW coal-fired power plant in Ekibastuz, Kazakhstan, covering 67,527 records for 14 indicators at 10 min intervals. Fractal R/S analysis confirmed long-term memory in SO2, PM2.5, and NOx series, guiding window length selection. The results show that the model achieves slightly better accuracy for SO2 (R2 0.95–0.38), while NOx and PM2.5 have similar dynamics (R2 0.93–0.26). However, accuracy drops notably after 12 points, making the model best suited for short-term forecasts. These findings support environmental monitoring services and help optimize plant parameters, contributing to lower emissions and advancing carbon neutrality goals. Full article
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18 pages, 1232 KiB  
Article
An EG-Tree Model Incorporating Spatial Heterogeneity for Analyzing Multifactorial Coupling Effects on Carbon Emissions Across Industries and Regions in China
by Jinrui Zang, Xin Hu, Kun Qie, Zian Zhang and Shi Zhang
Atmosphere 2025, 16(6), 663; https://doi.org/10.3390/atmos16060663 - 31 May 2025
Viewed by 339
Abstract
With the proposal of the dual carbon goals, it is of great significance to identify the causes of carbon emissions and reduce carbon emissions directly. There is a lack of analysis on the causes of carbon emissions considering the coupling effect of multiple [...] Read more.
With the proposal of the dual carbon goals, it is of great significance to identify the causes of carbon emissions and reduce carbon emissions directly. There is a lack of analysis on the causes of carbon emissions considering the coupling effect of multiple factors and regional heterogeneity. The causes of carbon emissions are examined from multiple perspectives utilizing the panel data spanning from 1997 to 2022, encompassing 30 provinces in China. To further analyze the causes of carbon emissions, an enhanced feature and regularized gradient boosting tree (EG-Tree) model is constructed, and a scoring method for the tree structure is proposed. The coupling effect of multiple factors are analyzed such as coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, liquefied petroleum gas, natural gas, etc., on the carbon emission intensity of various industries and their regional heterogeneity. The results show that: (1) The EG-Tree model constructed in this study could accurately analyze the causes of carbon emissions under the coupling of multiple factors based on the cumulative iterative feature branching contribution values (impact factors), with an average model fitting precision of 0.30. This means the carbon emission intensity values were predicted by various industries in different regions based on different energy consumption levels and industry-specific carbon emissions, compared with the carbon emission intensity values calculated using the carbon emission measurement dataset. (2) The consumption of coal and coke has a significant impact on the average carbon emission factors of various industries, with values of 7139.95 and 7217.05, respectively. The consumption of natural gas and liquefied petroleum gas has a smaller impact on the average carbon emission intensity of various industries under the EG-Tree model with corresponding carbon emission intensity impact factors of 5057.90 and 2789.57, respectively. (3) The Northeast region is a low-carbon area, while the East region is a high-carbon area, with total carbon emissions of 2,238,646.60 million tons and 5,566,314.00 million tons of CO2, respectively. The Northeast region has the lowest pollution intensity for heating and cooling, with carbon emissions of 155,661.73 million tons of CO2; the industrial carbon emissions in the East region are relatively high at 1,623,835.62 million tons of CO2. The research findings of this study are beneficial for relevant departments to focus on the main impact factors of carbon emissions in different regions and industries, and to develop targeted emission reduction policies. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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