Search Results (420)

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

Petroleum oily sludge (OLS), a hazardous by-product of the petroleum industry, and high-alkali lignite (HAL), an underutilized low-rank coal, pose significant challenges to sustainable waste management and resource efficiency. This study systematically investigated the combustion behavior, reaction pathways, and gaseous-pollutant-release mechanisms across varying blend ratios, utilizing integrated thermogravimetric-mass spectrometry analysis (TG-MS), interaction analysis, and kinetic modeling. The key findings reveal that co-combustion significantly enhances the combustion performance compared to individual fuels. This is evidenced by reduced ignition and burnout temperatures, as well as an improved comprehensive combustion index. Notably, an interaction analysis revealed coexisting synergistic and antagonistic effects, with the synergistic effect peaking at a blending ratio of 50% OLS due to the complementary properties of the fuels. The activation energy was found to be at its minimum value of 32.5 kJ/mol at this ratio, indicating lower reaction barriers. Regarding gas emissions, co-combustion at a 50% OLS blending ratio reduces incomplete combustion products while increasing CO₂, indicating a more complete reaction. Crucially, sulfur-containing pollutants (SO₂, H₂S) are suppressed, whereas nitrogen-containing emissions (NH₃, NO₂) increase but remain controllable. This study provides novel insights into the synergistic mechanisms between OLS and HAL during co-combustion, offering foundational insights for the optimization of OLS-HAL combustion systems toward efficient energy recovery and sustainable industrial waste management. Full article

In this study, we introduced a novel Trajectory-Channel Transport Model (TCTM) to unravel spatiotemporal dynamics of PM_2.5 pollution. By integrating high-resolution simulations from the Weather Research and Forecasting (WRF) model with the Nested Air-Quality Prediction Modeling System (WRF-NAQPMS) and 72 h backward-trajectory analysis, TCTM enables the precise identification of source regions, the delineation of key transport corridors, and a quantitative assessment of regional contributions to receptor sites. Focusing on four Yangtze River Delta cities (Hangzhou, Shanghai, Nanjing, Hefei) during a January 2020 pollution event, the results demonstrate that TCTM’s Weighted Concentration Source (WCS) and Source Pollution Characteristic Index (SPCI) outperform traditional PSCF and CWT methods in source-attribution accuracy and resolution. Unlike receptor-based statistical approaches, TCTM reconstructs pollutant transport processes, quantifies spatial decay, and assigns contributions via physically interpretable metrics. This innovative framework offers actionable insights for targeted air-quality management strategies, highlighting its potential as a robust tool for pollution mitigation planning. Full article

Soil quality assessment plays a critical role in promoting sustainable land management, particularly in fragile steppe ecosystems. This study provides a comprehensive geoecological evaluation of heavy metal contamination (Pb, Cd, Zn, Cu, Co, Ni, Fe, and Mn) in soils across five districts of the Akmola region, Kazakhstan. The assessment incorporates multiple integrated pollution indices, including the geochemical pollution index (Igeo), pollution coefficient (CF), ecological risk index (Er), pollution load index (PLI), and integrated pollution index (Zc). Spatial analysis combined with multivariate statistical techniques (PCA and clustering analysis) was used to identify pollutant distribution patterns and differentiate areas by risk levels. The findings reveal generally low to moderate contamination, with cadmium (Cd) posing the highest environmental risk due to its elevated toxic response coefficient, despite its low concentration. The study also explores the connection between current soil conditions and historical land-use changes, particularly those associated with the Virgin Lands Campaign of the mid-20th century. The highest PLI values were recorded in the Yesil and Atbasar districts (7.88 and 7.54, respectively), likely driven by intensive agricultural activity and lithological factors. PCA and cluster analysis revealed distinct spatial groupings, reflecting heterogeneity in both the sources and distribution of soil pollutants. Full article

High water quality in reservoirs used for drinking water supply and located within protected areas is of crucial importance for sustainable water-resource management. This study aims to evaluate the multi-depth water quality dynamics of the Ribnica Reservoir in western Serbia, combining two standardized assessment tools: the Serbian Water Quality Index (SWQI) and the Canadian Water Quality Index (CWQI). Data collected at various depths during 2021 and 2022 were analyzed to assess physico-chemical parameters and their impact on water quality, while the absence of microbiological data was noted as a limitation affecting the comprehensiveness of the assessment. The SWQI results indicated a general improvement in water quality over time, with values ranging from medium (82) to excellent (95) in 2021 and increasing from good (89) to excellent (98) in 2022. In contrast, the CWQI revealed specific risks, notably elevated concentrations of aluminum, mercury, and chromium, and reduced dissolved oxygen levels, with overall CWQI values ranging from poor (40) to good (88) depending on depth and parameter variability. The study highlights the necessity for continuous, comprehensive monitoring, including microbiological analyses and seasonal assessments, both within the reservoir and in the Crni Rzav River and its tributaries, to better understand pollutant sources and catchment influences. Strengthening microbiological and heavy metal monitoring, along with implementing proactive management strategies, is essential for preserving the Ribnica Reservoir’s ecological integrity and securing its long-term role in drinking water provision. Full article

Cacao is a food of global interest. Currently, the industry primarily utilizes the seed, which represents between 21% and 23% of the total fruit weight. In 2023, global production reached 5.6 million tons of fermented dry cacao beans, while approximately 25.45 million tons corresponded to cacao residues. The objective of this review was to compile and analyze alternatives for the utilization of cacao by-products. The methodology involved technological surveillance conducted in specialized databases between 2015 and 2025. Metadata were analyzed using VOSviewer software version 1.6.20. Priority was given to the most recent publications in high-impact indexed journals. Additionally, 284 patent documents were identified, from which 15 were selected for in-depth analysis. The reviewed articles and patents revealed a wide range of industrial applications for cacao by-products. Technologies including ultrasonic and microwave-assisted extraction, phenolic microencapsulation, cellulose nanocrystal isolation and targeted microbial fermentations maximize the recovery of polyphenols and antioxidants, optimize the production of high-value bioproducts such as citric acid and ethanol, and yield biodegradable precursors for packaging and bioplastics. The valorization of lignocellulosic by-products reduces pollutant discharge and waste management costs, enhances economic viability across the cacao value chain, and broadens functional applications in the food industry. Moreover, these integrated processes underpin circular economy frameworks by converting residues into feedstocks, thereby promoting sustainable development in producer communities and mitigating environmental impact. Collectively, they constitute a robust platform for the comprehensive utilization of cacao residues, fully aligned with bioeconomy objectives and responsible resource stewardship. Full article

Carbonaceous–siliceous–argillaceous rock-type uranium deposits, a major uranium resource in China, pose significant environmental risks due to heavy metal contamination. Geochemical investigations in the former Zoige uranium mine revealed elevated As, Cd, Cr, Cu, Ni, U, and Zn concentrations in soils and sediments, particularly at river confluences and downstream regions, attributed to leachate migration from ore bodies and tailings ponds. Surface samples exhibited high Cd bioavailability. The integrated BCR and mineral analysis reveals that Acid-soluble and reducible fractions of Ni, Cu, Zn, As, and Pb are governed by carbonate dissolution and Fe-Mn oxide dynamics via silicate weathering, while residual and oxidizable fractions show weak mineral-phase dependencies. Positive Matrix Factorization identified natural lithogenic, anthropogenic–natural composite, mining-related sources. Pollution assessments using geo-accumulation index and contamination factor demonstrated severe contamination disparities: soils showed extreme Cd pollution, moderate U, As, Zn contamination, and no Cr, Pb pollution (overall moderate risk); sediments exhibited extreme Cd pollution, moderate Ni, Zn, U levels, and negligible Cr, Pb impacts (overall extreme risk). USEPA health risk models indicated notable non-carcinogenic (higher in adults) and carcinogenic risks (higher in children) for both age groups. Ecological risk assessments categorized As, Cr, Cu, Ni, Pb, and Zn as low risk, contrasting with Cd (extremely high risk) and sediment-bound U (high risk). These findings underscore mining legacy as a critical environmental stressor and highlight the necessity for multi-source pollution mitigation strategies. Full article

Heavy metal pollution of lake sediments is one of the prominent ecological and environmental problems worldwide, and it is of great significance to conduct research on heavy metal pollution in lake sediments to protect the ecological environment, safeguard human health, and promote sustainable development. As an integral part of Dongting Lake, Datong Lake holds a crucial ecological position. More than 10 years ago, due to a series of factors, including excessive fertilizer application and fishing, the water quality of Datong Lake declined, resulting in varying degrees of contamination by Cd, Mn, and other heavy metals in the sediments. After 2017, Datong Lake began to establish a mechanism for protecting and managing the lake, and its ecological and environmental problems have been significantly improved. To clarify the current situation of heavy metal contamination in the sediments of Datong Lake, 15 sediment samples were collected from the lake, and the contents of soil heavy metals Cd, As, Pb, Cr, Cu, Mn, Ni, and Zn were determined. A Monte Carlo simulation was introduced to carry out the ecological and human health risk evaluation of the sediments in the study area to overcome the problem of low reliability of the results of ecological and human health risk evaluation due to the randomness and incompleteness of the environmental data as well as the differences in the human body parameters. The results and conclusions show that (1) the average values of Cd, Pb, Cr, Cu, Mn, Ni, and Zn contents in the sediments of Datong Lake are higher than the background values of soil elements in the sediments of Dongting Lake, and the average values of As contents of heavy metals are lower than the background values of the soil, and the heavy metal contamination in the sediments in the study area is dominated by slight contamination, and the possibility of point-source contamination is slight. (2) The results of both the Geo-accumulation index and Enrichment factor evaluation showed that the degree of heavy metal contamination of sediments was Ni > Cu > Cr > Mn > Cd > Pb > Zn > As. (3) The average value of the single ecological risk index of heavy metal elements, in descending order, was as follows: Cd > As > Pb > Cu > Ni > Cr > Zn > Mn; all the heavy metal elements were at the level of light pollution, and the average value of the comprehensive ecological risk index was 32.83, which is a slight ecological risk level. (4) Both non-carcinogenic and carcinogenic risks for all populations in the study area remain low following heavy metal exposure via ingestion and dermal pathways. Ecological and health risk assessments identified As and Cd as exhibiting significantly higher sensitivity than other heavy metals. Consequently, continuous monitoring and source-tracking of these elements are recommended to safeguard long-term ecological integrity and public health in the region. Full article

Identifying the soil quality and the sources of heavy metals in the production areas of characteristic agricultural products is crucial for ensuring the quality of such products and the sustainable development of agriculture. This research took the farmland soil of Luzuo Town, a characteristic production area of Cangshan garlic in Linyi City, as the research object. The contents of Cr, Cu, Ni, Pb, Zn, As, Hg, and Cd in farmland soil were analyzed. The ecological risks were evaluated using the Geographical Cumulative Index (I_geo) and the Potential Ecological Risk Index. The spatial distribution characteristics of the elements were determined through geostatistical analysis, and Positive Matrix Factorization (PMF) was used for source apportionment. The results show the following: (1) The average concentrations of all heavy metals exceeded local background values, with Cr and Hg surpassing the screening thresholds from China’s “Soil Pollution Risk Control Standards” (GB 15618-2018). (2) The results of the Moran’s index show that, except for Hg and Cd, all the elements had a high spatial autocorrelation, and there are two potential highly polluted areas in the study area. (3) Soils were generally uncontaminated or low risk, with Hg and Cd as the primary ecological risk contributors. (4) Five sources were quantified: fertilizer and pesticide sources (32.28%); mixed sources of fertilizer, pesticides, and manure (14.15%); mixed sources of traffic activities and agricultural waste discharge (19.95%); natural sources (20.55%); and incineration sources (13.07%). This study demonstrates the value of integrating geospatial and statistical methods for soil pollution management. Targeted control of Hg/Cd and reduced agrochemical use are recommended to protect this important agricultural region. Full article

Urbanization always leads to increasing challenges to the groundwater resources in karst regions due to intensive land use, infrastructure development, and the rapid transmission potential of pollutants. This study proposed an improved groundwater vulnerability assessment (GVA) framework by modifying the widely used COP (Concentration of flow, Overlying layers, and Precipitation) model, through the integration of three additional indicators: urban underground rail transportation (UURT), land use and cover (LULC), and karst development (K). Guiyang, a typical urbanized karst city in southwest China, was selected as the case study. The improved COP model, namely the COPK model, showed stronger spatial differentiation and a higher Pearson correlation coefficient ($r$) with nitrate concentrations ($r$ = 0.4388) compared to the original COP model (R = 0.3689), which validates the effectiveness of the newly introduced indicators. However, both R values remained below 0.5, even after model modification, suggesting that intensive human activities play a role in influencing nitrate distribution. The pollution load index (PI) was developed based on seven types of pollution sources, and it was integrated with the COPK vulnerability index using a risk matrix approach, producing a groundwater risk map classified into five levels. Global Moran’s I analysis (0.9171 for COP model and 0.8739 for COPK model) confirmed strong and significant spatial clustering patterns for the two models. The inclusion of UURT and LULC improved the model’s sensitivity to urban-related pressures and enhanced its capacity to detect local risk zones. It is a scalable tool for groundwater risk assessment in urbanized karst areas and offers practical insights for land use planning and sustainable groundwater management. Full article

Due to high geological background and intensive mining activities, soils are prone to heavy metals (HMs) accumulation and ecological fragility in Guizhou Province, China. A total of 740 topsoil samples were therefore collected, and aimed to determine the concentrations of As, Cd, Cr, Hg, and Pb, estimate the ecological pollution, and evaluate the carcinogenic and non-carcinogenic health risks to humans. Results showed As (1.08%) and Cd (24.46%) in soil exceeded standards. The Igeo showed that Cr (1.49%) and Hg (31.62%) in soil were at light pollution levels; single factor pollution index (PI) showed that Cd (21.35%) in soil was mildly polluted; risk index (RI) as at a low risk level. Notably, both deterministic and Monte Carlo analyses revealed unacceptable carcinogenic risks for As and Cr in children, with traditional methods potentially underestimating As risks. Moreover, Target-Organ Toxicity Dose (TTD) revealed soil HMs as a higher risk to hematological health, with notable health risks posed by Pb in children. It is noted that spatial distribution analysis suggested that the southwestern region of Guizhou Province should be prioritized for health risk management and control. By integrating the uniqueness of geological environments, multi-dimensional health risk assessments, and spatial distributions, the present study provides a scientific basis for assessing HMs pollution risks and soil health risks in the karst regions. Full article

Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. The model is trained here, using data from ten stations in Texas, enabling it to capture both spatial and temporal patterns in atmospheric behavior. Performance evaluation shows notable improvements, with a Root Mean Square Error (RMSE) reduction ranging from 34.11% to 71.63%. F1 scores for peak detection improved by up to 37.38%, Dynamic Time Warping (DTW) distance decreased by 72.77%, the Index of Agreement rose up to 90.09%, and the R² improved by up to 188.80%. A comparison of four loss functions—Mean Square Error (MSE), Huber, Asymmetric Mean Squared Error (AMSE), and Quantile Loss—revealed that MSE offered balanced performance, Huber Loss achieved the highest reduction in systematic RMSE, and AMSE performed best in peak detection. Additionally, four deep learning architectures were evaluated: baseline CNN-LSTM, a hybrid model with attention mechanisms, a transformer-based model, and an End-to-End framework. The hybrid attention-based model consistently outperformed others across metrics while maintaining lower computational demands. Full article

This article provides a systematic review of the current research status and latest progress in the field of mine ecological restoration. Using the SCI literature indexed by the Web of Science database as the data source, the research status and hotspots in the field of mine ecological restoration are displayed through the visual analysis of CiteSpace and the progress of mine ecological restoration technology this year is systematically summarized. Through a comprehensive review of existing technological methods, it is found that whether it is physical, chemical, biological restoration, or combined restoration technology, there are respective advantages, disadvantages, and application limitations. Physical remediation is a pretreatment, chemical remediation is prone to secondary pollution, while the sustainability shown by bioremediation makes it dominant in the of mine ecological remediation, but it has a long cycle and there is a risk of heavy metals that are accumulated by plants re-entering the biosphere through the food chain. Combined remediation can integrate the advantages of different restoration technologies and is the trend for the future development of mine ecological restoration. In the future, we should further promote technological innovation, perfect monitoring and evaluation technology, and promote informatization, scientization, and the effective implementation of mine ecological restoration, to achieve the ecological restoration and sustainable development of the mine area. Full article

Traditional single-scale indoor air quality (IAQ) evaluation methods often fail to meet the demands of modern, personalized kitchens. To address this limitation, we propose a comprehensive IAQ index, integrating experimental data and simulation results. The index incorporates four key IAQ auxiliary evaluation indicators: air distribution performance index (ADPI), predicted mean vote (PMV), cooking oil fume particulates (COFP), and CO₂ concentration. We developed a kitchen model and used the comprehensive IAQ index to benchmark simulation results against experimental tests. Optimal kitchen air quality occurred at a supply air angle of 90° and airflow velocity of 2.268 m³/min, reducing air pollution impact by 29.50%. This configuration enhanced thermal comfort while reducing secondary COFP accumulation in the breathing zone by 22%. The 29.50% Q-index reduction corresponded to a 24% decrease in peak CO₂ exposure (638 ppm, clean-air level) and 22% lower COFP in breathing zones, mitigating health risks. Optimized airflow (2.268 m³/min) avoided excessive ventilation, reducing energy waste and achieving balanced IAQ-energy efficiency. Full article

The accurate prediction of water quality parameters is essential for effective pollution control and resource management. This study presents a hybrid AI-remote sensing framework for forecasting water quality in the Gulf of Thailand, which combines Sentinel-2 imagery with Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) models. Our approach achieves a 5.4× increase in data coverage over traditional methods, demonstrating the effectiveness of machine learning in environmental monitoring. Predictive accuracy was evaluated across Support Vector Machine (SVM), ARIMA, and Amazon Forecast models. Results indicate that SVM, optimised through RBF kernel and grid search, outperforms other models for Chlorophyll-a (RMSE: 1.8), while ARIMA exhibits superior performance for Secchi Depth (RMSE: 0.2) and Trophic State Index (RMSE: 0.8). The study also introduces Aqua Sight, a web-based visualisation tool built on Google Earth Engine, enabling stakeholders to access real-time water quality forecasts. These findings highlight the potential of integrating satellite-derived data with machine learning to enhance early warning systems and support environmental decision making in coastal ecosystems. Full article