Search Results (9,759)

Urban soil contamination poses persistent risks to redevelopment, public health, and ecological restoration, yet actionable evidence is scattered across site investigation reports, monitoring databases, and regulatory documents. Existing decision-support tools often depend on manual searches and provide limited structured reasoning. This study develops a domain knowledge graph (KG) and a KG-powered question-answering (KBQA) system for urban soil management to organize multi-source evidence and deliver precise, auditable answers to parcel- and pollutant-specific queries. The approach (1) defines an urban soil ontology covering parcels, land uses, pollutants, measurements, pathways, and regulatory thresholds; (2) extracts and links entities and relations from textual and tabular sources; (3) constructs a graph database with provenance; and (4) implements a KBQA pipeline that maps natural-language questions to constrained graph queries and verbalizes results with citations. The resulting system supports source identification, land-use-specific exceedance checks, affected-parcel listing, and remediation reference retrieval. Experiments on a curated QA set and a South China case study show higher answer accuracy and lower latency than text-only baselines, while consistently returning traceable evidence and reducing cross-document lookup effort. Compared to text-only RAG baselines, the KG-powered system achieved a 0.14 improvement in Exact Match scores (e.g., 0.81 vs. 0.58 for Threshold tasks) and maintained a competitive median latency of 0.75 s. The pipeline utilizes a 13B-parameter instruction-tuned LLM. The ontology, schema, benchmark QA sets, and sample queries are publicly released to support transfer to other regions. Full article

Non-exhaust particulate emissions are expected to remain a relevant source of traffic-related air pollution, including an increase in electrified vehicle fleets. Particle formation results from tribological interactions and is influenced by both operating conditions and friction material system. This study presents an extended measurement methodology under application-relevant tribological conditions for the reproducible quantification of PM₁₀ and PM_2.5 emissions from dry-running friction systems and applies it to a systematic investigation of operating parameter and friction pairing effects. A dry inertial brake test bench with an enclosed friction chamber and integrated aerosol measurement chain was used under controlled tribologically relevant conditions. Specific friction work and specific friction power were varied by adjusting sliding velocity, contact pressure, and inertial load. Six friction pairings, comprising four representative friction lining types combined with either C45 cast steel or GGG40 gray cast iron, were examined. In situ PM₁₀ and PM_2.5 measurements were complemented by gravimetric wear and microstructural analyses. The results show that specific friction work has a direct influence on PM₁₀ and PM_2.5 emissions, whereas the independent effect of contact pressure is secondary. Friction power exhibits material-dependent effects. Emissions also vary strongly with friction pairing, indicating that operating conditions and material system must be considered jointly when assessing low-emission brake systems. Full article

Managing soil heavy metal pollution is pivotal for the sustainable development of the Beijing–Tianjin–Hebei (BTH) urban agglomeration. This study integrated geostatistical methods, Principal Component Analysis, and Positive Matrix Factorization (PMF) to characterize “source–sink” dynamics across diverse Main Functional Zones. Results revealed distinct pollution landscapes: Key Development Zones exhibited high-risk accumulation driven by multi-source superposition, while Ecological-restricted Zones, despite overall low pollution levels, faced significant anomalous enrichment of Cadmium (Cd). Source apportionment confirmed that this spatial differentiation stems from the coexistence of “in situ accumulation” and “source–sink misalignment” mechanisms. The former is driven by high-intensity industrial agglomeration, whereas the latter is governed by cross-boundary atmospheric transport and the topographic blocking of emissions from the plains. This research demonstrates for the first time the joint shaping effect of national spatial planning and natural geographical processes on regional pollution patterns. Accordingly, a precise management framework incorporating source reduction, cross-boundary synergy, and spatial reorganization is proposed, providing a new paradigm for addressing environmental risks caused by unbalanced development in rapidly urbanizing regions. Full article

To identify the sources and driving mechanisms of nitrate contamination in pore water around Nansi Lake, 54 pore water samples were analyzed via hydrogeochemical analysis, Gibbs diagrams, ionic ratios, and principal component analysis (PCA). The pore water is predominantly slightly alkaline, with dominant cations Ca²⁺ and Na⁺, and anions HCO₃⁻ and SO₄²⁻. Nitrate-nitrogen (NO₃⁻-N) concentrations range from 0.82 to 54.31 mg·L⁻¹, with a coefficient of variation of 1.41 and an exceedance rate of 18.52%, indicating significant external inputs. A positive correlation between NO₂⁻ and NO₃⁻ suggests denitrification in some areas. Nitrate concentrations exhibit distinct spatial heterogeneity: high concentrations occur in agricultural/aquaculture lakeside plains and urban areas, low concentrations near coal mining subsidence zones, and transitional zones showing outward diffusion. Nitrate sources are predominantly anthropogenic. High Cl⁻ and low NO₃⁻/Cl⁻ ratios indicate domestic and aquaculture wastewater infiltration, whereas low Cl⁻ and high NO₃⁻/Cl⁻ ratios indicate agricultural fertilizer input. Industrial and natural sources are minor. PCA identified three controlling factors (cumulative variance 69.81%): coal mining and industrial/domestic pollution (39.82%), carbonate rock weathering (19.44%), and agricultural activities (10.55%). Health risk assessment shows no significant risk for adults (hazard quotient (HQ) < 1), but children face localized risks at nine sites (HQs of 1.25–2.26) in intensive farming, urban, and transitional zones. Excessive fertilizer application and sewage leakage are the primary causes, posing methemoglobinemia risks to infants. This study provides a scientific basis for nitrate pollution control and sustainable water management in the Nansi Lake Basin and offers methodological insights for similar lacustrine plain regions. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

The synergistic control of multiple pollutants is critically challenged by complex nonlinear interactions, strong spatiotemporal heterogeneity, and the difficulty of tracing causal drivers. Deep learning offers high predictive power but suffers from the “black-box” problem, limiting its acceptance in environmental decision-making. Explainable Deep Learning (XDL) integrates physical mechanisms with interpretable algorithms, achieving both prediction accuracy and explanatory transparency. This review systematically evaluates the effectiveness and limitations of XDL in analyzing multi-pollutant interactions, with a comparative focus on atmospheric and aquatic environments. Key techniques, including SHAP, attention mechanisms, and physics-informed neural networks, are examined for their roles in synergistic monitoring, source apportionment, and regulatory optimization. The main findings reveal that: (1) XDL, particularly the “tree model + SHAP” paradigm, has become a dominant tool for quantifying driving factors, yet most attributions remain correlational rather than causal; (2) physics-informed fusion (soft vs. hard constraints) improves physical consistency but faces unresolved conflicts between data and physical laws, with current models lacking a conflict detection mechanism; (3) cross-media comparison shows a unified technical logic of “physical mechanism guidance + post hoc feature attribution”, but atmospheric applications lead in embedding advection–diffusion constraints, while aquatic research excels in spatial topology modeling via graph neural networks; (4) critical bottlenecks include the lack of causal inference, uncertainty-unaware interpretations, and data scarcity. Future directions demand a shift from correlation-only to causal-aware attribution, from blind fusion to conflict-detecting systems, and from no evaluation standards to domain-specific validation benchmarks. XDL is poised to transform multi-pollutant governance from experience-driven to intelligence-driven approaches, provided that verifiable interpretability and physical consistency become core design principles. Full article

This article analyses the impact of ground-source heat pumps on the energy efficiency and environmental impact of a residential building, depending on the temperature of the ground source, the flow temperature of the heating system, and the domestic hot water temperature. In the section on heating, the best results were obtained for the 35/28 °C system at a ground temperature of 1 °C, for which the SCOP was 4.81, the system efficiency was 3.90, the final energy was 5.6 kWh/m²yr, and the primary energy was 13.9 kWh/m²yr. The least favourable heat pump heating option was recorded for the 55/48 °C system and a ground temperature of −1 °C, with a SCOP of 3.31 and a primary energy of 17.4 kWh/m²yr. For domestic hot water, the best results were achieved at a temperature of 47 °C and a ground temperature of 1 °C, for which the SCOP reached 3.88, and the primary energy was 23.6 kWh/m²yr, whereas the least favourable variant (55 °C/−1 °C) was characterised by a SCOP of 3.31 and primary energy of 27.5 kWh/m²yr. It was demonstrated that lowering the temperature on the upper-source side improves the system’s energy performance to a greater extent than changes in ground temperature. At the same time, improvements in energy efficiency reduced pollutant emissions and lowered the equivalent emission ZrSO₂ index. The results confirm that the actual energy and environmental potential of ground-source heat pumps depend primarily on their integration with low-temperature systems. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

The transport sector is a major contributor to urban air pollution and greenhouse gas emissions in Pakistan, posing significant challenges to sustainable development and climate commitments. This study develops the first technology-resolved, high-resolution, multi-pollutant emission inventory and scenario analysis for Pakistan’s transport sector, addressing key gaps in previous studies that lacked integrated multi-pollutant assessments, comprehensive coverage of non-road sources, and long-term scenario comparisons. The analysis integrates road and non-road transport sources within the Greenhouse Gas–Air Pollution Interactions and Synergies (GAINS) modeling framework. Emissions are projected for 2024–2050 under a business-as-usual (BAU) scenario and three mitigation pathways: an Electric Vehicle Transition (EVT) emphasizing transport electrification, a Euro-VI scenario focusing on stringent fuel and vehicle emission standards, and an integrated nationally determined contribution strategy (NDC+) scenario combining electrification, regulatory improvements, and structural transport reforms. In 2024, transport-related emissions are estimated at approximately 22 kt of fine particulate matter (PM_2.5), over 300 kt of nitrogen oxides (NO_x), and nearly 39 Mt of carbon dioxide (CO₂), alongside substantial emissions of other gaseous pollutants and short-lived climate forcers. By 2050, the NDC+ scenario achieves the largest reductions relative to business-as-usual, demonstrating that coordinated electrification and emission control strategies can simultaneously reduce air pollution and greenhouse gas emissions. The results demonstrate strong synergies between climate mitigation and air quality improvement, showing that integrated strategies combining electrification with stringent emission standards can simultaneously reduce greenhouse gas emissions and major air pollutants while advancing cleaner and more sustainable mobility. This analysis provides a consistent and policy-relevant evidence base derived from best-available data and modeling tools to support Pakistan’s NDC implementation, sustainable mobility planning, and integrated air quality and climate strategies, with lessons transferable to other rapidly developing economies. Full article

Background: Dermatological conditions represent a leading cause of global nonfatal disease burden, accounting for approximately 42.9 million disability-adjusted life years annually. Their complex pathogenesis is increasingly understood through the skin–brain–exposome axis, a bidirectional neuroimmunological and environmental communication network. The study aims to synthesize the neurobiological mechanisms of the skin–brain–exposome axis with macroscopic sociodemographic modifiers, clinical manifestations, and evidence-based psychodermatological interventions. Methods: A narrative review was conducted, following a structured search of PubMed, Scopus, and Web of Science (from inception to February 2026), yielding 54 sources. Mechanistic and interventional data (including randomized controlled trials and meta-analyses) were integrated with large-scale population-based epidemiological findings, anchored by a recent cross-sectional Polish cohort of 27,000 adults. Results: Psychological distress is associated with hyperactivation of the hypothalamic–pituitary–adrenal (HPA) axis and peripheral neurogenic inflammation (e.g., Substance P, corticotropin-releasing hormone), exacerbating stress-sensitive conditions such as atopic dermatitis, psoriasis, acne, and chronic pruritus. External exposome factors (urbanization, pollution) and sociodemographic variables (education, gender) may modify biological risk and diagnostic capture rates, frequently generating an epidemiological diagnostic paradox. Randomized trials support that psychotherapeutic interventions, particularly Cognitive Behavioral Therapy (CBT) and Mindfulness-Based Stress Reduction (MBSR), effectively disrupt the physical itch–scratch–stress cycle and improve disease-specific quality of life, serving as evidence-based adjunctive strategies in comprehensive care. Conclusions: Effective dermatological management requires targeting both the cutaneous barrier and the psychological exposome. Integrating routine psychosocial screening and stratified behavioral interventions into standard clinical care is essential for addressing the neuroimmune chronicity of inflammatory skin diseases. Full article

Pb–Zn slag and smelting activities represent a persistent global source of soil contamination, releasing toxic heavy metals—lead (Pb), zinc (Zn), cadmium (Cd), and arsenic (As)—with documented risks to ecosystems and human health. Although previous reviews have addressed heavy metal contamination near smelters and pollution indices as assessment tools, no review has specifically mapped environmental assessment methods for Pb–Zn slag-contaminated soils, and evidence from Central Asia remains absent. This scoping review, following PRISMA-ScR 2018 guidelines, maps the global evidence base on soil contamination from Pb–Zn slag and associated assessment methods. Searches across Dimensions, PubMed, and OpenAlex identified 410 records; 56 studies (2010–2025) met the inclusion criteria. Studies were concentrated in China (35.7%), Poland (8.9%), and Brazil (7.1%); no studies from Kazakhstan were identified despite major Pb–Zn smelting operations in the Shymkent region. All studies reported heavy metal concentrations exceeding regulatory thresholds, with cadmium as the primary ecological risk driver and lead posing the greatest health risk to children. Assessment methods included pollution indices (73.2%), ecological risk assessment (67.9%), GIS-based spatial analysis (57.1%), human health risk frameworks (51.8%), and source apportionment models (50.0%). Post-2018 studies increasingly applied integrated multi-method frameworks. Critical gaps include the absence of Central Asian research, limited predictive modeling, and a lack of standardized protocols. Findings provide a structured evidence map to guide environmental monitoring and remediation at slag-contaminated sites globally. Full article

The location of the decommissioned factory of plastics and chemical products Jugovinil, City of Kaštela, Croatia, has gained significant attention for urban development and the establishment of tourist facilities over the past three decades. Since the site is on the coast of the Adriatic Sea, on the shore of Kaštela Bay, where nautical tourism is already developed, plans for a five-star tourism complex were initiated. Given that the former industrial plant, its coal-powered power plant, and other later industrial activities (small shipyards) caused a certain degree of contamination with NORM (naturally occurring radioactive material) residues and heavy metals, an on-site detailed investigation was conducted into the spatial distribution and concentration evaluation of contaminants within dozens of soil samples, and the distributions of contaminants in the area of interest were shown in the form of maps. This study applies an integrated GIS and geostatistical framework to analyze the spatial distribution of multiple contaminants. Maps highlighting polluted zones are included, along with maps indicating areas with higher cumulative concentrations of contaminants. This paper provides an overview of potential issues related to the detected contaminants, as well as proposals for remediation methods before repurposing the site using retrospective data about sources of residues and contaminants. Full article

As major sources of pollution and carbon emissions, energy enterprises have long faced challenges in their technological transformation due to the industry’s characteristics of high investment costs and strong lock-in effects. While formal mechanisms such as government subsidies can impose short-term constraints, they fail to stimulate the sector’s intrinsic motivation. Can the reputation incentive mechanism be the key to breaking the deadlock? This paper constructs a three-party evolutionary game model involving energy enterprises, the public, and the government from the perspective of informal institutions. For the first time, it incorporates the dual effects of reputation gains and losses into a unified framework. The results show that reputation incentives are not merely a “cherry on top,” but rather independently drive transformation by moderating enterprises’ cost–benefit structures. The evolution of the three-party strategies exhibits dynamic synergy, and the system equilibrium depends on the threshold matching of key parameters. Subsidy policies are effective in the short term, but may crowd out the role of reputation in the long term. This paper reveals the underlying logic by which the integration of informal institutions and formal regulations drives profound transformation, offering new theoretical perspectives and practical guidance for designing incentive-compatible multi-stakeholder governance frameworks. Full article

Atmospheric particulate matter poses a high risk by carrying potentially toxic components such as polycyclic aromatic hydrocarbons (PAHs). The major sources of these potentially toxic compounds include traffic-related emissions and winter heating, implying the combustion of fossil fuels or biomass. Air pollution, especially chronic exposure, poses the most serious human health hazard in childhood, and several studies emphasise the importance of research on the potential impacts of air pollution in school environments. While indoor air quality studies are already available in Hungary, investigations on outdoor air pollution in school environments are missing. To fill this gap, in a medium-sized Hungarian town, Veszprém, six schools were selected to assess air quality in the outdoor environments where schoolchildren spend their breaks and have physical training. These schools represent different locations and conditions, from high-trafficked sites to suburban environments. Using resuspended dust samples, environmental quality was assessed based on PAH contents of the samples and ecotoxicity tests (Vibrio fischeri bacterial bioassay). Ecotoxicity of the samples moved in a wide range, from highly toxic to non-toxic. PAH measurements indicated considerable contamination in the case of one sample taken from a suburban area. Source apportionment demonstrated that winter heating is also an important pollution source. Full article

The solar-assisted air source heat pump (SAHP) is a key technology of low carbon heating. However, the SAHP is still inefficient and unstable at low temperatures. Cascaded latent heat storage (CLHS) can store multi-stage thermal energy, which provides the possibility for the multiple utilization of solar energy. Hence, this paper proposed the SAHP integrated with CLHS for building heating. The high-temperature and medium-temperature latent heat storage (LHS) units are used for direct heating, and the low-temperature LHS unit preheats the air for the air source heat pump (ASHP). The thermal performance of the CLHS device is evaluated through combined numerical simulations and experimental tests. Results show that the average heat storage rate of the cascaded system is 61.1% higher than that of a conventional single-stage LHS unit. The heat storage uniformity of CLHS gradually improves with increasing inlet flow rate, but shows a trend of first increasing and then decreasing with the increase in fluid inlet temperature. Among the three tested levels, 80 °C was found to be the most uniform heat storage of the CLHS device. The performance of the system was further analyzed using TRNSYS to assess seasonal building heating performance. The overall efficiencies of the high/middle/low temperature LHS units are 93.6%, 81.6% and 94.3%, respectively. And the solar heat supply accounts for 70.8% of the total heat supply of the system. Compared with the non-preheating system where the low-temperature LHS unit is removed, the COP of the graded heating system is increased by 18.3%, and the energy consumption is reduced by 16.6%. Further parametric optimization based on the Hooke–Jeeves method reduces total system energy consumption by 20.7% and associated pollutant emissions by 20.6% compared with the pre-optimization system. The findings provide practical insights into the application of CLHS in solar-assisted heat pump systems for building heating. Full article