Search Results (3,903)

Recent studies have shown that common household activities produce characteristic patterns in indoor air pollutants, enabling activity inference using environmental measurements alone. However, pollutant-based approaches are usually formulated as flat multi-class classification problems, even though indoor environments are dominated by long baseline periods with no emission-generating activity, leading to false alarms and unstable predictions. This work proposes a gated hierarchical inference framework for recognizing activities from indoor air quality data. A first-stage gate detects whether a time window contains activity-induced pollutant dynamics, while a second-stage classifier conditionally identifies the specific activity only when activity relevance is detected. Multivariate time-series measurements of particulate matter, volatile organic compounds, nitrogen oxides, carbon dioxide, temperature and relative humidity were collected using a portable monitoring system during controlled household cooking and cleaning experiments. Temporal windows were processed using recurrent neural network models in both stages. By separating activity detection from activity identification, the proposed method aligns inference with the physical generation of indoor pollutant signals and improves robustness in baseline-dominated monitoring scenarios while maintaining reliable discrimination among activities. The framework supports unobtrusive activity recognition and enables applications in exposure-aware monitoring and intelligent indoor environmental management. Full article

Quantifying anthropogenic CO₂ increments is vital for assessing emission reductions. Using a seamless XCO₂ dataset over China reconstructed from OCO-2/3 satellite retrievals and machine learning, combined with EOF decomposition and LISA analysis, this study investigates XCO₂ anomalies and local anthropogenic increments (dXCO₂) at national and urban agglomeration scales. Nationally, XCO₂ anomalies exhibit a “southeast positive, northwest negative” spatial pattern aligning with human activities and a “winter high, summer low” seasonal cycle. EOF analysis reveals four dominant modes: anthropogenic–natural trade-offs, East Asian summer monsoon modulation, local emissions, and baseline context. At the regional scale, multi-year mean dXCO₂ (2015–2019) in Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are 3.46 ± 0.45 ppm, 1.30 ± 0.36 ppm, and 0.08 ± 0.14 ppm, respectively, showing higher values in northern heavy industrial zones. During the 2020–2022 pandemic, dXCO₂ decreased in BTH (2.28 ± 0.73 ppm) and YRD (1.16 ± 0.43 ppm) but increased in PRD (0.28 ± 0.27 ppm). Compared to pre-pandemic levels, lockdowns saw dXCO₂ decrease slightly in YRD while increasing in BTH and PRD, reflecting differential responses of regional industrial structures. This study demonstrates the potential of seamless XCO₂ data for monitoring anthropogenic enhancement signals, and the proposed LISA-based method offers new support for regionally differentiated emission reduction assessments. Full article

Global energy de-fossilization requires scalable solutions for extended energy storage and industrial emission reduction. Synthesizing green methane via Power-to-Gas technology offers a viable pathway to store renewable electricity while utilizing captured carbon dioxide. This review evaluates recent advancements in catalytic mechanisms, reactor engineering, artificial intelligence applications, and techno-economic and life cycle assessments of green methane production systems. Analysis shows that advanced reactor configurations effectively manage the exothermic heat of the Sabatier reaction. Furthermore, integrating machine learning algorithms accelerates catalyst discovery and enables dynamic process control under fluctuating renewable energy loads. Economic and environmental assessments indicate that the sustainability of green methane depends strictly on utilizing renewable electricity and sourcing non-fossil carbon. Commercial deployment must focus on improving catalyst stability during transient operations and implementing digital twins to establish green methane as a sustainable carbon backbone for chemical industries. Full article

Maintaining asphalt pavements requires substantial quantities of materials and energy, which significantly contribute to greenhouse gas emissions in the road infrastructure sector. This study quantified the carbon dioxide equivalent (CO₂e) emissions associated with a maintenance and rehabilitation plan for an asphalt pavement using a simplified life-cycle perspective integrated with the Highway Development and Management Model (HDM-4). The methodology combined HDM-4 to define a 35-year intervention plan (2022–2057) with CO₂e emission factors for three quantified components: material production, transportation, and construction machinery operation. The approach was applied to a 7.8 km section of the Mexicali–San Felipe highway in Baja California, Mexico. The results indicate that the intervention plan generated approximately 2483.9 t CO₂e over the 35-year analysis period. Reconstruction was the most carbon-intensive activity, accounting for 1890 t CO₂e, while milling and overlay generated 292.15 t CO₂e per direction. Material extraction and production were the dominant sources of emissions, contributing about 70% of the total emissions in milling and overlay and 60% in reconstruction; in the latter case, transportation also represented a substantial share (35%) due to long haul distances. These findings show that the proposed approach can identify the most emission-intensive activities and processes within pavement maintenance plans and provide quantitative environmental criteria to support more sustainable road management decisions. Full article

It is known that a significant portion of global carbon dioxide (CO₂) emissions originate from concrete production. However, construction and demolition activities result in a considerable amount of construction and demolition waste (CDW). The proper recycling of CDW is important in terms of conserving natural resources and ensuring sustainability. A significant amount of recycled concrete aggregate (RCA) is obtained from the recycling of CDW. Many researchers have contributed to reducing carbon emissions by conducting studies on RCA. However, the fact that recycled aggregates (RAs) are obtained from different construction wastes is the biggest obstacle to generalizing the studies in the literature. This study aims to identify machine learning (ML) models that can reliably predict the compressive strength of concrete produced with recycled concrete aggregates (RCAs) and to evaluate the impacts of their use. In this study, keywords (15) obtained from articles (7953) selected from Web of Science were searched in the Scopus database. The selected studies (397) were analyzed using VOSviewer (version 1.6.20) software to identify leading institutions, countries, authors, sources, fields, gaps, challenges, and trends related to the use of recycled aggregate in concrete. This study not only has a theoretical structure but also makes a significant contribution to the literature by offering practical recommendations for field applications. This is the most important feature that distinguishes this study from other research. This study also promotes the use of RAs in concrete to reduce CO₂ emissions and encourages its sustainable use in the construction sector. Full article

Rivers emit substantial amounts of carbon dioxide (CO₂) to the atmosphere, yet its response to heavy rainfall remains unclear with intensive anthropogenic disturbances. To fill the knowledge gap, this study investigated the dynamic variability of CO₂ partial pressure (pCO₂) and CO₂ emissions flux at the Chaohu Lake Basin, a watershed under intensive anthropogenic perturbations, based on field campaigns across diverse river systems during dry season, normal season, and post-rainfall periods. Results demonstrated marked differences in aquatic pCO₂ across river types, with urban rivers (3949 µatm) exhibiting significantly higher levels than non-urban counterparts (1423 µatm). Rainfall events elevated riverine pCO₂, but the effect size varied between river types (urban river versus non-urban river). In non-urban rivers, pCO₂ following heavy rainfall (2461 μatm) was significantly higher (p < 0.05) than those observed during both dry season (1096 μatm) and normal season (712 μatm). In contrast, urban rivers demonstrated only marginal pCO₂ elevation after rainfall (20–30%). Statistical analysis revealed that discharge, total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH₄⁺-N) showed significantly positive correlations with pCO₂, while dissolved oxygen (DO) and pH exhibited significantly negative correlations with pCO₂. Overall, rivers in the Chaohu Lake Basin act as significant sources of atmospheric CO₂, with an annual mean CO₂ emission flux of 297.84 mmol·m⁻²·d⁻¹, and the heavy rainfall events amplify riverine CO₂ emissions (629.91 mmol·m⁻²·d⁻¹), with observed enhancement effects exceeding 300% compared to baseline conditions. To accurately estimate the CO₂ emissions from human-dominated rivers, future research should emphasize the impacts of extreme or heavy rainfall events. Full article

South Asia has experienced a persistent rise in per capita carbon dioxide emissions despite growing policy attention to low-carbon development. Against this background, this study examines how economic growth, energy intensity, renewable energy, urbanization, and trade openness shape per capita carbon dioxide emissions in six South Asian countries over the period 1990–2023. Grounded in the STIRPAT framework, the analysis combines fixed-effect estimation with two-step system generalized method of moments to address unobserved heterogeneity, endogeneity, and emissions persistence. The results show that economic growth remains strongly carbon-intensive, with gross domestic product per capita exhibiting a near-proportional elasticity with emissions. Energy intensity significantly increases emissions, while renewable energy reduces them. Urbanization exerts a positive but smaller effect, whereas trade openness remains statistically fragile. The findings also indicate strong emission persistence, underscoring the importance of early intervention. The study contributes to the regional environmental literature by providing an integrated and dynamic assessment of South Asia’s growth–energy–emissions nexus and by introducing a composite policy-support dimension into the empirical framework. The results offer practical implications for energy efficiency reform, renewable expansion, and climate-sensitive urban policy in developing economies. Full article

Understanding carbon dioxide (CO₂) emissions from buildings is critical for shaping effective policies toward sustainable urban development. Previous studies mainly applied bottom-up methods for small areas or top-down downscaling at national, provincial or grid scales. However, limited research has explored the relationship between building functions and CO₂ emissions at a larger scale. To bridge this gap, this study employed ridge regression to disaggregate monthly CO₂ emissions to the level of different functional buildings across England in 2022 and investigated the relationship between building functions and CO₂ emissions. Results show that commercial buildings rank highest in CO₂ intensity, reaching 1.49 kg per volume in February, while residential buildings rank lowest, reaching 0.25 kg per volume in July at the national scale, and industrial buildings have the largest total emissions. In addition, regional disparities in economic development and industrial structure contribute to emission differences among buildings of the same function. Temporally, all functional buildings exhibited lower emissions during summer compared to winter. Overall, this study offers a scalable and interpretable framework for understanding urban carbon emissions at high spatial and functional granularity. The findings may offer valuable insights to support government decision-making in urban planning and spatial policy design, thereby contributing to low-carbon development goals. Full article

The effectiveness of policy tools used to combat global environmental degradation and climate change is gaining importance. The role of environmental policies in reducing carbon dioxide emissions and whether rising emissions levels lead to tightening of environmental policies are the main research questions of this study. Using panel causality methods, this study examines the relationship between the Environmental Policy Stringency Index (EPS) and carbon dioxide (CO₂) emissions across 23 OECD countries from 1991 to 2020. The results of the Dumitrescu–Hurlin panel causality test show bidirectional causality between EPS and CO₂. According to the asymmetric causality test, we find causality from EPS to CO₂ during positive shocks. Furthermore, when examining negative shocks, we report a unidirectional causality originating only from EPS in the US. Our results demonstrate that the bidirectional relationship between EPS and CO₂ can contribute to the development of effective and balanced environmental policies if policymakers and society consider this interaction. Full article

Renewable energy sources, such as solar and biomass, represent sustainable alternatives to meet the growing energy demands of the residential sector. This study evaluated the energy, exergy, and environmental performance of two Brayton configurations using supercritical carbon dioxide: a recompression cycle (SRC) and a recompression cycle with intercooling in the main compression (SMC), both coupled to a dual-loop organic Rankine cycle (DORC) and powered by a hybrid solar-biomass thermal system. Mass, energy, and exergy balances were developed, and a life cycle assessment was performed to quantify the environmental impact. The systems were designed to cover a cooling load of 130 kW corresponding to 200 dwellings constructed with Asbestos cement in the Colombian Caribbean region. The results show that both configurations meet the required demand; the SMC-DORC cycle operates at 650 °C, while the SRC-DORC requires 750 °C. The SRC-DORC exhibits higher thermal efficiency (53.24%), while the SMC-DORC achieves a slightly higher exergy efficiency (28.15%). Environmental analysis shows that the construction phase accounts for the majority of the total impact, exceeding 95% of emissions. Overall, both configurations are technically feasible, with the SRC-DORC standing out for its balance between efficiency and environmental impact. Full article

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises. Full article

Rapid urbanization has led to a significant increase in land use carbon emission (LCE), putting great pressure on ecological security. The coupling relationship between LCE and the ecological security index (ESI) is the key to sustainable development. Based on land use/cover change (LUCC) and Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) data, the LCE of the Jilin Border Cities (JLBCs) from 2013 to 2023 was estimated. Twenty-seven indicators were selected from both natural and socioeconomic aspects to evaluate the ESI using the Driving forces–Pressure–State–Impact–Response–Management (DPSIRM) model. The spatial interaction between LCE and ESI was analyzed using the coupling degree model and spatial autocorrelation. The results show that from 2013 to 2023, the main LCE areas in the JLBCs were concentrated in central urban districts, while the total LCE remained negative but exhibited a clear upward trend. The ESIs in Tonghua City and Baishan City have continued to improve, but those in Yanbian Autonomous Prefecture have gradually deteriorated, with ecological security warnings intensifying progressively toward the east. The spatial variation in the LCE–ESI coupling degree is significant, predominantly exhibiting low coupling with differences across scales. Within the study area, coupling degree shows a strong positive correlation, revealing distinct spatial clustering patterns dominated by low clusters and cold spots. Future efforts should focus on promoting low-carbon development models, strengthening protection and restoration, while implementing targeted measures to enhance the overall ecology of JLBCs. Full article

Novel carbon-storing construction materials have the potential to reduce greenhouse gas emissions by removing carbon dioxide from the atmosphere and storing it in long-lived building products. In order to understand the full benefits and shortcomings of carbon-storing materials, life cycle assessments (LCAs) must be performed. However, material innovators who are looking to perform LCAs of their products during early-stage research and development (R&D) face many challenges. While these challenges have been reported in the literature, this information has been fragmented and required a more comprehensive investigation. We explored these LCA challenges by holding an in-person workshop with sixteen R&D teams who were developing carbon-storing materials and building designs. From the data collected in this workshop, we found that the R&D teams struggled with data availability, biogenic carbon, and uncertainty, which confirmed our findings from the literature. They also struggled with various other LCA topics. Since current LCA tools lack functions that would be useful for this user group, we also proposed a list of tool ideas that could address their LCA needs, which can inform future LCA tool development. Full article

The continuous augmentation of greenhouse gas and pollution emissions has exerted a conspicuous and negative influence on social production, economic development, and human health. As the digital economy continues to penetrate into various fields of social development, whether the advancement of the digital economy can promote urban pollution and carbon dioxide emission reduction has emerged as a pivotal topic of interest across all sectors of society. This study adopts empirical research methods to delve into the direct static, dynamic effects, spatial effects, and spatial spillover effects of the digital economy on pollution and carbon dioxide emission reduction in the Yangtze River Delta Urban Agglomeration (YRDUA). As evidently suggested by the research findings, the digital economy has an inverted U-shaped impact on carbon dioxide and pollution emissions. As heterogeneity analysis reveals, this inverted U-shaped influence relationship exhibits heterogeneous effects in the high-level group and low-level group of digital economy development. The robustness of this conclusion was demonstrated through robustness testing. Mechanism analysis reveals that, in the early stage of digital development, infrastructure expansion serves as the primary channel driving emissions, whereas in the later stage, green technological progress becomes the key mechanism enabling emission reductions. Finally, the results confirms that digital economy has a significant negative spatial correlation effect on carbon dioxide and pollution emissions, and has an inverted U-shaped spatial spillover effect on neighboring regions. Full article

Landfills are a significant source of atmospheric emissions associated with the decomposition of organic waste; however, conventional monitoring methods typically have limited spatial coverage. This study evaluates the use of an UAV-based system for the spatial characterization of gases associated with biogas emissions at a municipal landfill. A DJI Matrice 350 RTK platform equipped with a Sniffer4D Mini2 multi-gas station and a Zenmuse H20T thermal camera were used. Four flight campaigns were conducted at an altitude of 20 m, with an acquisition frequency of approximately 1 Hz, recording total hydrocarbons (C_xH_y) as an indirect indicator of methane (CH₄), carbon dioxide (CO₂), carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), oxygen (O₂), temperature, and relative humidity. The results showed a marked transition around 13:10 h, characterized by a simultaneous increase in CH₄ equivalent and CO₂, along with a decrease in NO₂, O₃, and SO₂. Furthermore, CH₄ equivalent and CO₂ showed the highest positive correlation among the variables (r = 0.96). Spatial maps generated using ordinary kriging revealed more heterogeneous patterns, while the qualitative thermal orthophoto confirmed the site’s surface variability. Overall, the results demonstrate that the integration of multi-gas sensors and aerial thermography on UAVs is viable for the spatial monitoring of landfills. Full article