Search Results (478)

This paper demonstrates how detailed acoustic micro-segmentation can serve as a vital decision-support tool, providing localized, evidence-based data required to effectively integrate environmental acoustics and foster community co-design in urban rail modernization planning. Furthermore, while physical noise barriers are widely praised as standard mitigation products in transport engineering, our analysis highlights their severe inherent limitations, including spatial constraints, high costs, and visual pollution, making them highly unsuitable for dense historical fabrics. As viable alternatives, we propose the implementation of active, source-targeted engineering technologies (e.g., rail absorbers, modernized track beds) alongside dynamic operational governance. To be effective, these alternative solutions must be sourced and embedded directly into the earliest stages of infrastructural project documentation. Ultimately, this paper demonstrates that evidence-based acoustic governance is an essential institutional pillar for long-term resilience, providing interdisciplinary perspectives that inform future sustainable transport policy and practice. Full article

Urban environments concentrate spatially distributed stressors, including heat, noise, pollution, and biodiversity loss, that are increasingly recognized as determinants of population mental health. However, current approaches rarely integrate environmental structure, lived exposure, and climate-related emotional responses within a unified spatial framework applicable to public health. This article introduces Eco-Affective Health Mapping (EAHM) as a conceptual, spatially explicit framework, grounded in the recently formalized Eco-Affective Health theoretical model, for understanding how environmental conditions may shape climate-related emotional responses, including eco-anxiety, solastalgia, and ecological grief, across urban socio-ecological systems. Drawing on evidence from spatial epidemiology, landscape ecology, environmental mental health, and digital phenotyping, I argue that affective responses to environmental stressors are not randomly distributed but are hypothesized to exhibit spatial clustering in relation to environmental exposures, landscape configuration, and mobility-based interactions. I propose the Eco-Affective Health Mapping (EAHM) framework, which integrates four spatial layers: environmental exposures, landscape configuration, person–place interaction, and affective indicators, together with a companion composite metric, the Eco-Affective Load Index (EALI), for which I provide a formal multi-domain specification and a purely illustrative, non-empirical worked example. EAHM is presented here as a theoretical and methodological proposal rather than as a validated instrument: no primary environmental, mobility, or affective data were collected or analyzed for this article, and the framework’s constituent relationships require prospective empirical testing, for which I outline a companion measurement strategy grounded in the Eco-Affective Health Assessment Protocol (EAHAP). By conceptualizing climate-related emotional responses as candidate measurable public health signals, EAHM is intended to support a future shift toward prevention-oriented, population-level mental health strategies aligned with planetary health and sustainable development agendas. Full article

Urban noise pollution is considered to be a significant challenge to neighborhood livability, particularly in cities with limited noise governance. This has resulted in raising the environmental noise as a critical health and livability concern. Existing approaches rely on decibel thresholds that fail to capture the spatial, social and perceptual complexity of noise conditions at the neighborhood scale. This study offers a multi-criteria soundscape framework integrating acoustic measurements and urban parameters (urban design, landscape, regulations and perception). The aim is to support the optimal location for noise planning. The framework was applied as a pilot study across five residential neighborhoods in Tripoli, Lebanon. The OpeNoise mobile application was used across four temporal sessions. Recordings were combined with qualitative resident interviews. Average Equivalent Continuous Sound level LAeq(t) values ranged from 60.3 to 76.8 dBA. While all neighborhoods exceeded the World Health Organization WHO guidelines, acoustic severity alone did not determine the best case for intervention. The neighborhood with the highest measured noise did not achieve the highest priority score. This demonstrates that decibel planning is insufficient. The alignment of acoustic severity, feasibility, and community demand is more effective for intervention. The framework’s criteria are based on observable urban indicators, suggesting applicability to similar urban contexts, further pending validation. Full article

Accurate forecasts of O₃, NO₂, and SO₂ from regulatory daily summary records support early warning and environmental management, whereas the inherent nonlinearity, non-stationarity, and multi-scale fluctuations of pollutant sequences severely hinder prediction precision. This paper develops a hybrid CEEMDAN-VMD-GRU-AC model for one-step-ahead concentration prediction of three typical air pollutants. Chronologically ordered valid daily summary records from the Los Angeles–North Main Street monitoring station from 2015 to 2025 were used for evaluation. The original pollutant sequences are first decomposed into multi-scale intrinsic mode functions via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Variational mode decomposition (VMD) is further applied to refine high-frequency IMF1 and IMF2 to decouple irregular short-term oscillations. A gated recurrent unit (GRU) equipped with an additive correction (AC) branch is constructed to predict each decomposed component, and all subseries predictions are aggregated to obtain the final concentration forecasts. The evaluation was conducted under an offline full-series decomposition protocol, in which CEEMDAN and VMD were applied to the complete pollutant sequence before the chronological training–validation–test split; therefore, the reported metrics should not be interpreted as fully prospective rolling-origin forecasting performance. Because the extracted records were not reindexed to a complete daily calendar, the 30-step input represents 30 consecutive valid records rather than 30 uninterrupted calendar days. Compared with benchmark prediction methods, the proposed framework yields the lowest RMSE and MAE, together with the highest R² values of 0.9646, 0.9516, and 0.9035 for O₃, NO₂, and SO₂, respectively. The ablation results reveal pollutant-dependent effects: VMD refinement provides the largest improvement for O₃, whereas the final GRU-AC configuration performs best for NO₂ and SO₂. These results demonstrate the feasibility of the proposed framework under the stated offline protocol, while calendar-complete rolling-origin and multi-station validation remains necessary before operational deployment. Full article

This study reviewed 187 articles based on systematic review guidelines to evaluate pollution controls through a novel analytical multimedia framework that bridges air, water, soil, and acoustic compartments, alongside emerging digital and circular economy paradigms. With 5–7% of worldwide CO₂ emissions originating in the cement industry, fugitive particulate matter constitutes more than 90% of a plant’s emissions. The results show that the cement sector has considerable potential for decarbonization, though highly context-dependent. Under optimal conditions, clinker substitution coupled with alternative fuels could reduce direct emissions by up to 50% and total energy usage by 44%, constrained by regional material availability. Fully integrated carbon capture systems (TRL 7–9) could reduce exhaust emissions by up to 90%, contingent upon overcoming significant energy penalties. Engineering controls in dry-process mills reduced daily occupational noise exposure from 102.9 to 88.3 dB(A). Regarding soil pollution, cement kiln dust increased the unconfined compressive strength of native soils up to 9.9 times for geotechnical stabilization. In water management, hybrid biological systems removed 94.5% of particulate matter and reduced oxygen demand by over 87%, while advanced biomonitoring decreased effluent toxicity by over 90%. The significance of this study lies in overcoming traditional siloed assessments by introducing a holistic multimedia framework that maps biogeochemical interconnectivity alongside Industry 4.0 paradigms. Ultimately, this review provides a vital sociotechnical road map for stakeholders to align localized ecological risk mitigation with stringent 2026 global market mechanisms, such as the carbon border adjustment mechanism and mandatory environmental, social and governance disclosures, ensuring both industrial competitiveness and environmental stewardship. Full article

The accurate prediction of hydrodynamic characteristics and structural responses in underwater fishing gear is critical for optimizing design, ensuring operational safety, and minimizing environmental impact. To overcome the computational costs and scalability limitations of traditional physical modeling, this study evaluates three machine learning algorithms such as Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine with a Radial Basis Function kernel (SVM-RBF) to predict the hydrodynamic coefficients and structural responses of tuna longline components, including mainlines and branch lines. Models were trained and validated using a comprehensive flume tank dataset encompassing six gear configurations tested across varying flow velocities and lead-line weights. Results demonstrate that optimal model selection is inherently task dependent. For hydrodynamic coefficients, LightGBM achieved superior predictive accuracy for branch-line drag (whole-dataset R² = 0.8315), while both LightGBM and SVM-RBF excelled in lift prediction. Conversely, structural responses (sinking depth and x-displacement) proved inherently more difficult to model deterministically due to high-frequency transient dynamics and stochastic variability. While LightGBM provided balanced generalization for sinking depth, SVM-RBF exhibited severe overfitting for x-displacement. In contrast, RF maintained the most conservative and consistent performance across structural targets, effectively mitigating the memorization of dynamic noise observed in the more complex algorithms. Beyond predictive modeling, feature importance analysis identified flow velocity, lead-line weight, material stiffness, and geometric parameters as dominant physical drivers, validating the physical plausibility of the models. Crucially, the integration of experimental and ML analyses revealed that a polylactic acid (PLA)-integrated midsection configuration consistently yielded the lowest and most stable drag force (0.004–0.13 N at 0.49 m/s), representing a 30–60% reduction compared to conventional nylon lines. Furthermore, the study uncovered novel physical phenomena, including velocity-independent deformation stability, progressive transient sinking kinetics, and tension-induced load redistribution. These findings establish machine learning as a reliable, scalable surrogate for longline gear design, advocating for thin-diameter, biodegradable PLA-integrated lines to enhance hydrodynamic efficiency and mitigate marine plastic pollution, while underscoring the necessity of task-specific algorithm selection for robust engineering applications. Full article

Environmental noise has emerged as a pervasive yet frequently underestimated environmental stressor with consequences for biodiversity, public health, and societal well-being. Although a substantial body of epidemiological research has linked chronic transportation-related noise exposure with cardiovascular disease, sleep disturbance, psychological distress, impaired cognitive performance, and premature mortality, comparatively few studies have estimated its broader economic burden within a planetary health framework. This study presents an exploratory order-of-magnitude economic valuation based on a large-cohort epidemiological risk-transfer scenario. A hazard-ratio-based framework was applied to estimate noise-attributable mortality among populations subjected to road traffic environmental noise levels above 60 dB. Both tangible costs, representing forgone economic productivity, and intangible costs, representing societal welfare losses using the Value of a Statistical Life framework, were estimated. Under baseline assumptions, chronic transportation-related environmental noise was associated with approximately 27,692 annual attributable deaths—when applying a hazard-based ratio—estimated annual productivity losses of approximately US$7.28 billion and welfare losses valued at approximately US$353.91 billion under the Value of a Statistical Life framework. These findings suggest that chronic transportation-related environmental noise represents a potentially important, though often overlooked, environmental externality with substantial health and economic implications. The proposed framework provides an initial basis for future research evaluating the wellbeing, societal, and economic magnitude of environmental noise within a planetary health context. Full article

Anthropogenic noise represents a significant form of environmental pollution, with consequences for both human health and ecosystem functioning. The aim of this study was to critically assess the adequacy of current environmental standards in light of the scale and nature of anthropogenic noise impacts on human health and ecosystem functioning, with particular emphasis on forested and protected areas. The methodology included a review of the scientific literature, an analysis of legal and institutional documents from the European Union and Poland, and a comparison with selected international approaches, in particular the practices of U.S. national parks, where the soundscape is regarded as an environmental resource requiring active protection. The review findings indicate that, in humans, exposure to noise is associated with, among other effects, an increased risk of cardiovascular disease, sleep disturbances, impaired cognitive functioning, and reduced psychological well-being, with nighttime noise being of particular significance. At the same time, numerous field and experimental studies demonstrate that, in many taxa, behavioral and physiological responses occur at levels lower than the thresholds applied to protect human health; ecologically significant effects have been reported at levels as low as approximately 40 dB, particularly in species dependent on acoustic communication. The analysis of existing regulations reveals the absence of operational standards and criteria for protected areas understood as natural habitats, which hinders the effective management of the acoustic climate. The proposed values of 40 dB for the nighttime period and 50 dB for the daytime period should be treated as indicative precautionary thresholds resulting from a qualitative synthesis of the literature and requiring empirical validation across different habitat types and geographical contexts. Their implementation may support the achievement of sustainable development objectives by simultaneously protecting human health, preserving biodiversity, and strengthening ecosystem resilience to anthropogenic pressures. Full article

Urban environments expose individuals to multiple stressors, including air pollution and noise, which significantly impact health by causing cardiovascular and respiratory diseases and sleep disruption. Effective monitoring of these stressors through intelligent sensing technologies can support the mitigation of long-term deterioration in both physical and mental health. In this context, this pilot study presents a multimodal approach that integrates environmental sensing and physiological monitoring to assess stress responses of the human body to urban conditions. Indoor and outdoor air quality were measured using smart sensor nodes that captured particulate matter (PM₁, PM_2.5, PM₄, PM₁₀), air temperature and relative humidity. The physiological response to urban noise exposure was evaluated using electrodermal activity (EDA) and heart rate variability (HRV) acquired via a wearable biomedical device, while sound pressure levels (dBA) were measured using a professional sound level meter. Preliminary results indicate that indoor particulate matter concentrations greatly exceeded outdoor levels, despite outdoor sensors being deployed in a high-traffic urban environment. Physiological analysis revealed increased tonic electrodermal activity under noise exposure, indicating increased sympathetic activation. Complementary HRV analysis showed elevated heart rate (HR), reduced parasympathetic activity, and increased sympathetic dominance under high-noise conditions, confirming a measurable physiological stress response to urban environmental exposure. Full article

This study investigated indoor environmental air quality (IEAQ) across university workspaces at a higher education institution in Sharjah, United Arab Emirates (UAE), assessing environmental conditions that may influence occupant health, the surrounding environment, and sustainability. Physical parameters (temperature, relative humidity, noise, and illuminance), chemical parameters (indoor gases and particulate matter), and biological contaminants (airborne bacteria and fungi) were measured in semi-occupied indoor environments with a total of 68 random samples collected and analyzed. Perceived heat discomfort and environmental variability were assessed using the Thom Discomfort Index (TDI), Humidex Index, ANOVA, Kruskal–Wallis, Mann–Whitney U, and one-sample t-tests. Average measurements of relative humidity, temperature, noise, and illuminance were 60.7%, 21.6 °C, 57.5 dB, and 440 lux, respectively. Average concentrations of PM_2.5, PM₁₀, CO, and CO₂ were 1223 ppm, 104 ppm, 1 ppm, and 623 ppm, respectively. Microbial contamination was generally insignificant across most investigated workspaces. While most measured parameters remained within recommended threshold limit values (TLVs), elevated levels of noise, illuminance, and particulate matter were observed in selected workspaces. These findings demonstrate that university indoor environments generally maintain acceptable air quality conditions; however, targeted interventions, including improved HVAC maintenance and indoor pollutant management, are required to enhance sustainable university indoor environments and optimize occupant comfort. Full article

The Rio Santiago basin is one of the most environmentally stressed river systems in Mexico, with persistent organic, nutrient, microbial, surfactant, and metal contamination. This study develops a near-term quantum machine learning workflow for environmental monitoring and water-pollution profiling using multivariate records from 13 stations between 2009 and 2022. QML is evaluated here because quantum feature maps can define nonlinear, interaction-rich kernels that remain executable on present quantum hardware, providing an alternative representation to compare with classical PCA, RBF, UMAP, and HDBSCAN baselines rather than a presumed computational advantage. After quality screening, log transformation, standardization, and domain-guided feature selection, pollution profiles are evaluated across PCA, RBF spectral clustering, UMAP/KMeans, UMAP/HDBSCAN, a simulated ZZ-style quantum feature-map kernel, and Qiskit Runtime hardware evaluations of the same kernel concept. The initial cleaned-data results show that classical PCA clustering identifies broad lower-load, high organic/surfactant, and rain-season solids/microbial profiles. UMAP/HDBSCAN provides the strongest cleaned full-sample nonlinear baseline, with a silhouette score of 0.568 after excluding 177 noise samples. The simulated quantum-kernel representation separates station-linked gradients, while matched n = 650 stability diagnostics show near-identical quantum-kernel clustering across random initializations (mean ARI = 0.994 for cleaned data) but retain the RBF kernel as the strongest nonlinear comparator. Two 24-sample Qiskit hardware runs and two matched 8-record hardware checks provide proof-of-execution evidence. The analysis is framed as a controlled representation study, not as a claim of quantum advantage. Full article

Fireworks have been used in China for more than a millennium, though they are an increasing part of celebration globally. Consumption of fireworks is on the rise despite increased regulation of their use. This review examines the key themes that are apparent in contemporary research: contamination of air, water and soil, in addition to waste debris, noise and light pollution, along with contemporary approaches to mitigate environmental impact. Research is, as expected, more frequent from countries with high fireworks use, so some rather small countries such as the Netherlands, Malta and Iceland are notably active. Concentrations of emitted gases (especially SO₂) and fine particles are frequently studied, along with associated toxic metals and semimetals (especially Cu, Zn, Cd, As, Ba and Sr). There are many projections of effects of fireworks, but relatively few epidemiological studies of health outcomes or the impact of contamination on local ecosystems. Fireworks waste and debris is an environmental problem; it is expensive to clear and aesthetically unpleasing. Excessive noise (up to 137 dB) created by fireworks affects pets and wildlife, as well as posing a risk to pyrotechnicians. Fireworks produce bursts of light that can be distracting to motorists and disturb wildlife, while smoke particles cause lowered visibility. Green fireworks and festivals of light with lasers or drone technology present routes to lower impact. Contemporary society is sympathetic towards restricting fireworks, but recognition of their cultural importance remains. Full article

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

Post-disaster reconstruction generates extraordinary demand for construction materials, often necessitating the rapid deployment of temporary concrete production facilities. While these systems are operationally essential for rebuilding, their environmental and public health impacts remain insufficiently examined through structured and reproducible analytical approaches. This study develops an integrated qualitative-dominant environmental risk assessment framework combining systematic documentary analysis, environmental pathway modeling, semi-quantitative risk scoring, and comparative benchmarking against established environmental health standards. Focusing on the reconstruction process following the 2023 Kahramanmaraş earthquakes in Türkiye, the study identifies and evaluates major environmental exposure pathways, including particulate matter emissions, wastewater discharge, soil degradation, and noise pollution. A semi-quantitative risk assessment model based on probability, severity, and exposure duration is applied to classify the relative intensity of identified environmental risks under post-disaster operational conditions. The findings demonstrate that accelerated reconstruction processes, emergency regulatory flexibility, and rapid industrial deployment substantially amplify cumulative environmental pressures in already vulnerable post-disaster environments. In response, the study proposes an integrated governance and engineering framework aimed at reducing environmental impacts while maintaining reconstruction efficiency. Methodological transparency is ensured through explicit documentation of data sources, screening procedures, analytical criteria, and risk classification logic. The study also acknowledges the limitations associated with restricted access to primary field measurements in post-disaster environments and therefore adopts a triangulated documentary and comparative analytical strategy. The proposed framework offers a transferable model for evaluating temporary industrial infrastructures in post-disaster reconstruction systems globally. Full article

This review paper examines innovative urban design strategies for sustainable noise management through a structured analysis framed by ten guiding questions. It begins with an overview of conventional noise assessment technologies and progresses to advanced mitigation approaches. Core principles of sustainable urban design are explored, alongside evaluations of urban and transportation planning, traffic-reduction measures, green infrastructure, and resilient architectural strategies. Material innovations and modern noise-control technologies are presented as complementary solutions. Community-based methods, including citizen science and participatory planning, are highlighted for fostering inclusive governance. The discussion concludes by addressing key challenges and future directions, underscoring interdisciplinary collaboration to transform urban noise pollution into opportunities for healthier, more livable cities. Full article