Search Results (3,110)

Air and water pollution pose critical threats to public health and environmental stability, particularly in rapidly urbanizing developing nations. This study investigates synergistic interactions between air and water pollutants across 14 cities in Hunan Province, China (2020–2024), using multiparametric statistical approaches. The results show that the coefficient of variation (CV) of particulate matter (PM) with diameters less than 2.5 μm (PM_2.5, CV = 46.9%) and turbidity (TU, CV = 47.4%) showed the highest variability among the air and water quality parameters, respectively. Annual trends revealed significant increases in ozone (O₃) alongside decreases in carbon monoxide (CO) and nitrogen dioxide (NO₂) concentrations. Concurrently, freshwater systems exhibited rising electrical conductivity (EC), water temperature (WT), and pH, paired with declining levels of ammonia nitrogen (NH₃-N), total phosphorus (TP), and turbidity (TU). Principal component analysis (PCA) and Spearman correlation analyses showed significant positive correlations between PM and nitrogen species (TN, NH₃-N), but negative correlations with TP, suggesting potential cross-media pollution interactions. Cross-correlation analysis revealed significant time-lagged relationships (1–5 months) between atmospheric pollutants and aquatic nutrients, suggesting that atmospheric deposition may serve as a contributing pathway for cross-media contamination. The study not only provides empirical evidence for integrated pollution control strategies in urbanizing watersheds, but also offers a transferable framework for addressing similar air–water quality interactions on a global scale. Full article

The Pearl River Delta region suffers from congestion in the urban road network, noise, air pollution, and other “urban diseases”. Vigorously developing inland water transportation can greatly alleviate these “urban diseases”. However, it is difficult to take advantage of the inland waterway transportation cost advantages due to the Pearl River Delta’s short haul distance characteristics. In recent business practice, a novel, environment-friendly, and competitiveness-enhanced inland waterway transportation mode has emerged in the area, called the leftover-cargo mode in this paper. This mode is composed of first-tier (big companies) and second-tier (small companies) inland barge companies, which establish a cooperative relationship and jointly meet the needs of shippers and can lead to a modal shift from inland truck to inland waterway transportation. In real practice, the pricing methods of this novel mode still rely on experience. We propose four pricing game theory models based on channel leadership in order to investigate how decision-making impacts the pricing and income of the two-tier companies. We find that, if the market price ceiling is low, second-tier inland barge companies always benefit more than first-tier companies, which is very interesting and counter to the existing literature. These findings offer pricing insights into economically viable leftover-cargo cooperation and its role in supporting sustainable road-to-waterway freight modal shift in the Pearl River Delta. Full article

Traffic congestion and air pollution caused by rapid urbanization have emerged as critical challenges in metropolitan areas worldwide. Urban air mobility (UAM), particularly electric propulsion-based systems, has gained attention as a promising solution. For the successful commercialization of UAM, a lightweight airframe design with ensured structural integrity is essential. This study proposes an optimized lightweight design process that integrates automated composite manufacturing with artificial intelligence (AI)-based material property prediction. Finite-element analysis (FEA) was performed on glass fiber-, basalt fiber-, and carbon fiber-reinforced polymers under identical deformation conditions to derive design material properties in terms of elastic modulus and weight reduction. A large-scale dataset of fiber-reinforced plastics was established through an automated manufacturing process, and a deep learning regression model was developed using Altair AI Studio to predict mechanical properties under untested material and process conditions. The predicted properties were applied to a UAM fuselage model, and FEA results demonstrated that composite structures achieved equivalent or superior stiffness with up to 50% weight reduction compared to aluminum. In addition, inverse reserve factor (IRF) analysis confirmed structural safety, with all configurations maintaining IRF values below 1. The proposed AI-driven framework provides a scalable and data-driven lightweight design methodology applicable to next-generation UAM and advanced air mobility structures. Full article

Against the backdrop of China’s “dual carbon” goals and urban green transition, improving urban land use efficiency is essential for shifting land development from extensive expansion to intensive and low-carbon use. Using the Low-Carbon City Pilot Program as a quasi-natural experiment, this study examines panel data from 285 prefecture-level cities in China from 2007 to 2023. We apply a multi-period difference-in-differences model, a threshold regression model, and a spatial Durbin model to assess the program’s impact on urban land use efficiency. The results show that the pilot program significantly improves urban land use efficiency, and the effect persists over time. This finding remains robust across a series of robustness checks. Heterogeneity analysis shows that the efficiency gains are stronger in cities with lower air pollution control pressure, higher industrial pollution control pressure, and lower fiscal pressure. Further threshold analysis shows that digital connectivity is a key condition for strengthening the policy effect. The spatial analysis suggests that the policy effect shows some spatial association. However, the decomposed indirect and total effects are not robust, so the spatial results should be interpreted with caution. This study provides empirical evidence on how low-carbon city pilots affect urban land governance and land use efficiency. Its conclusions, however, remain subject to limitations related to efficiency measurement, policy identification, and the availability of city-level data. Full article

The construction sector is undergoing rapid digital transformation, creating opportunities to enhance environmental safety in urban areas. One critical application lies in air pollution forecasting, particularly regarding fine dust (PM₁₀) emissions. While machine learning (ML) models are widely used for city-wide air quality monitoring, a significant research gap exists in the high-resolution (5 min interval) forecasting of dust at localized “point-pattern” development sites. These densely built urban zones present unique challenges due to highly volatile microclimates and intermittent emission sources that directly affect nearby residents. The purpose of this study is to perform a preliminary performance analysis of eight predictive algorithms—ARIMA, EMA, Prophet, NNAR, Random Forest, SVM, and XGBoost—to identify the most robust approach for short-term PM₁₀ forecasting under limited data (N = 1728). Special attention is paid to the non-linear relationship between meteorological conditions and dust concentrations. Unlike previous studies which focused on general urban backgrounds, this work contributes a validated methodological framework for localized monitoring. The results demonstrate that tree-based ensemble models provide the highest stability and accuracy, offering a reliable basis for future real-time environmental management and active pollution mitigation strategies on urban construction sites. Full article

Reducing carbon emissions while improving air quality is a central challenge for rapidly urbanizing regions. Focusing on 31 prefecture-level cities in the Middle Reaches of the Yangtze River Urban Agglomeration, this study examines carbon–pollution synergy (CPS), spatial dynamics, and the driving factors of CO₂ and representative air pollutants from 2013 to 2023. Spatial autocorrelation analysis, a revised four-factor Logarithmic Mean Divisia Index (LMDI) decomposition, and a factor-based CPS assessment were used to identify spatial clustering, compare driver heterogeneity, and evaluate coordination between CO₂ and primary pollutants. To improve methodological consistency, the LMDI decomposition and CPS assessment focus on the primary pollutants SO₂, CO, and NO₂, whereas PM_2.5 and O₃ are retained in the spatial analysis and discussion because they are strongly affected by secondary formation, atmospheric transport, and meteorological conditions. The results show that CO₂ and the selected pollutants exhibit significant but pollutant-specific spatial clustering. High CO₂ values remain concentrated in the core cities of Wuhan, Changsha, and Nanchang, PM_2.5 shows a persistent north–south gradient, and SO₂ hotspots shift from traditional industrial cores toward peripheral areas receiving industrial relocation. The revised LMDI results show that economic development is the most stable positive driver of CO₂ and the primary pollutants, whereas the energy-consumption factor generally suppresses emissions. The recalculated population-scale factor fluctuates around 1, indicating a comparatively limited and stage-dependent contribution once the other factors are controlled for. CPS analysis further indicates that coordinated reduction is most robust under the energy-consumption factor and, for conventional combustion-related pollutants, also under the energy-structure factor. Overall, the region has a clear basis for CPS governance, but effective implementation requires pollutant-specific and region-specific control strategies rather than a uniform co-mitigation pathway. Full article

The concept of architecturally mediated allostatic overload has established that chronic exposure to stress-inducing built environments can elicit stress responses within the body, overwhelming regulatory systems and contributing to adverse health outcomes through sustained activation of stress response pathways. Recent advances in epigenetics, combined with emerging evidence of environmental stress-induced epigenetic modifications, suggest that the health impacts of chronic built environment stress may extend far beyond previously understood physiological consequences. This paper introduces the theoretical concept of “built environment-modulated epigenetics” (BEME), extending the framework of architecturally mediated allostatic overload to consider how chronic exposure to stress-inducing built environments may create lasting epigenetic modifications with potential transgenerational implications. We propose that prolonged activation of the hypothalamic–pituitary–adrenal (HPA) and sympathetic-adreno-medullary (SAM) axes by built environment stressors may result in maladaptive DNA methylation and histone modifications affecting stress-responsive genes, similar to documented effects of environmental toxins, air pollution, and psychosocial stressors. Given robust evidence that environmental stressors can create transgenerational epigenetic effects, this theoretical framework suggests that stress-inducing built environments may impact not only current occupants, but future generations through heritable epigenetic modifications. This extension of architecturally mediated allostatic overload theory fundamentally challenges traditional approaches to architectural design and urban planning, positioning the built environment as a potential determinant of long-term epigenetic programming. Full article

Free-floating bike-sharing (FFBS) systems play a valuable role in alleviating traffic congestion and reducing carbon emissions, making them vital to sustainable urban transportation. Although extensive research has investigated the relationship between the built environment and cycling behavior, the adverse effects of air pollution and its interaction with the built environment remain insufficiently understood. In this study, multisource data from Shenzhen are used, and an XGBoost–SHAP model is employed to comprehensively investigate the nonlinear associations among the FFBS trip volume, built environment, and air pollution while considering the spatial heterogeneity in interaction effects. The results indicate that population density, road density, building density, and PM2.5 are the most influential factors. In addition, significant temporal heterogeneity is observed between weekdays and weekends. The effects of the built environment variables and their interactions are more pronounced on weekdays than on weekends. More importantly, an interaction analysis reveals that the positive influence of compact urban development on cycling is conditional: in high-density areas with elevated pollution exposure, the health risks associated with air pollution can offset or even outweigh the mobility benefits of compactness. Overall, this study identifies the complex, spatially heterogeneous mechanisms through which the built environment and air quality jointly shape FFBS usage. These findings provide important evidence for integrating environmental health considerations into compact city planning and offer practical insights for promoting cycling and sustainable urban mobility in high-density cities. Full article

Receptor modeling of ambient pollutant concentrations plays a central role in urban air quality assessments. This study proposes an integrated framework combining Self-Organizing Maps (SOM), Positive Matrix Factorization (PMF), and Time-Series Analysis (TSA) for a comprehensive evaluation of urban air pollution patterns and source dynamics. The methodology was applied to multi-annual air quality and meteorological datasets (2009–2018) from two major Bulgarian cities, Plovdiv and Varna. The SOM was used for assessing the overall parameter patterns of the cities, leading to a clear clustering of the site samples on the map. Thus, PMF was run separately for the two sites, identifying a different number of sources (three and four, respectively). Traffic-related and sulfur-rich combustion sources were identified in both cities, while a crustal/resuspended dust factor was observed only in Varna. TSA revealed distinct temporal behaviors among source types. Traffic-related aerosol contributions decreased in both cities (−5.14% yr⁻¹ in Plovdiv; −9.30% yr⁻¹ in Varna), whereas sulfur-rich combustion factors showed increasing trends (+4.64% yr⁻¹ and +2.97% yr⁻¹, respectively). Traffic fresh exhaust factors exhibited pronounced seasonal variability and significant weekday–weekend differences in both cities. The integrated SOM–PMF–TSA framework enhanced source interpretability and temporal characterization, providing a robust approach for urban air quality assessment and supporting targeted air pollution management strategies. Full article

Landscape pattern is closely associated with pollution in rapidly urbanizing watersheds, but most studies still focus on single pollutants or single environmental media. This study developed a watershed-based framework to compare coupled water and air pollution in the Liaohe River Basin, China. A total of 156 hydrologically connected sub-basins were used as common spatial units. Landscape metrics were calculated for 2000, 2010, and 2020. Total nitrogen and total phosphorus loads were simulated using the Soil and Water Assessment Tool, while annual mean PM_2.5 and O₃ concentrations were aggregated from gridded products to the same sub-basin scale. Coupling coordination degree was used to identify relative co-pollution patterns within the aquatic and atmospheric systems. GeoXGBoost with spatial block cross-validation was used to evaluate predictive performance, and GeoSHAP was used to interpret model-based predictor contributions. The aquatic coupled pollution index was predicted more accurately than the atmospheric index, indicating a stronger landscape association with nutrient coupling. Cropland proportion was the most stable predictor of aquatic coupling, whereas forest proportion was the most stable predictor of atmospheric coupling. These results suggest that water-oriented management should focus on cropland structure and ecological buffering, while air-oriented management should emphasize forest continuity and fragmentation control. The framework provides a spatially explicit basis for differentiated watershed management and territorial spatial planning. Full article

The Metropolitan Area of São Paulo (MASP), located in southeastern Brazil, faces significant air quality challenges due to its large vehicle fleet and complex fuel composition, including widespread ethanol use. Air pollution dynamics in this context are investigated, focusing on spatio-temporal variations in formaldehyde (HCHO) and nitrogen dioxide (${\mathrm{NO}}_2$), and their role in ozone (${\mathrm{O}}_3$) formation. High-resolution data from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite are used to analyze HCHO and ${\mathrm{NO}}_2$ vertical column densities (VCDs) over a 5-year period (2019–2023). Results reveal high HCHO and ${\mathrm{NO}}_2$ VCDs over MASP, with spatial patterns related to land use and higher concentrations during the dry season, with HCHO mean VCD reaching 14.21 × ${10}^{15}$ molecules ${\mathrm{cm}}^{-2}$ and ${\mathrm{NO}}_2$ mean VCD reaching 8.91 × ${10}^{15}$ molecules ${\mathrm{cm}}^{-2}$. The Formaldehyde to Nitrogen dioxide Ratio (FNR) thresholds were derived based on observations from 24 CETESB surface ${\mathrm{O}}_3$ monitoring stations, providing region-specific constraints for ${\mathrm{O}}_3$ sensitivity classification in MASP, with lower and upper thresholds of 1.6 and 2.4. Based on these thresholds, the analysis indicates a predominance of VOC-sensitive conditions in the urban core, alongside transition and NOx-limited regimes in other areas. Full article

Sustainable transport management (STM) has become an increasingly important issue in recent years, as cities have faced growing traffic congestion, air pollution, and other transport-related challenges. Travel time (TT) represents one of the critical determinants of Quality of Service (QoS) and user satisfaction in public passenger transport (PPT). TT extends beyond in-vehicle duration and encompasses a sequence of temporal components, including access, waiting, transfer, and egress times. TT reflects the complexity of an integrated passenger transport system (IPTS), where users experience transport services as a door-to-door journey rather than isolated trips. This article analyses the TT within IPTSs through the lens of European quality standards EN 13816 and EN 15140 for PPT. Standard EN 13816 provides a normative framework for defining TT as a key QoS criterion reflecting user expectations and a user-oriented perspective, while standard EN 15140 operationalises this framework by specifying methodological requirements for the measurement and evaluation of the delivered TT quality at system-level performance objectives. This research highlights a structural gap between the conceptualisation of TT as a door-to-door journey, a user-oriented phenomenon, and its measurement through fragmented, mode-specific performance metrics. It limits the ability of transport authorities and operators to accurately evaluate the QoS and to design efficient urban mobility (UM) systems. Full article

In this study, we present a machine-learning approach—a land use random forest (LURF) model—to produce daily PM₁₀ concentration maps at a 1 km resolution across the Campania region for the year 2022. The model combines daily measurements from 13 ARPA Campania monitoring stations with a wide set of spatial and atmospheric information. The predictors include population, land cover, road network, ERA5 meteorological data, satellite aerosol observations from MODIS, output from the CHIMERE chemistry transport model, and a flag identifying days affected by Saharan dust transport. The model is trained and validated using a station-based cross-validation scheme that accounts for spatial correlation between sites. Under this scheme, the LURF reproduces observed concentrations with substantially smaller errors than the raw CHIMERE output (RMSE of 11.0 vs. 23.6 $\mathsf{\mu }$g m⁻³). CHIMERE concentrations and ERA5 meteorology emerge as the most informative predictors, while the dust flag specifically improves the representation of episodic high-PM₁₀ events. The resulting 1-km maps reveal clear urban–rural contrasts. They identify pollution hotspots in the Naples metropolitan area and along major motorways that are not visible in coarser model outputs. Probabilistic exceedance maps further show that meeting the future 2030 EU limit value of 20 $\mathsf{\mu }$g m⁻³ will be challenging across much of the metropolitan area. Overall, the proposed framework provides a low-cost, practical tool for high-resolution PM₁₀ exposure assessment, supporting epidemiological studies, environmental justice analyses, and air quality management in regions with complex terrain and limited monitoring coverage. Full article

Amid rapid industrialization and urbanization, air pollution has emerged as a major public health concern linked to non-communicable diseases (NCDs), with older adults particularly vulnerable. Beyond its direct physiological effects, social participation could buffer environmental health risks by enhancing resilience, encouraging healthy behaviors, and reducing stress. Using data from the 2020 China Longitudinal Aging Social Survey (CLASS; 11,398 respondents aged 60 and above), linked with county-level air pollution indicators (PM_2.5, O₃, SO₂, NO₂, and CO), this study applied multilevel models to examine the association between air pollution and NCD prevalence among older adults, as well as the mediating role of social participation. Results show that higher NO₂ concentrations significantly increased NCD risk (OR = 1.27, 95% CI: 0.87–1.73), whereas higher SO₂ concentrations (mean = 9.96 µg/m³, ranged from 5.69 to 19.99 µg/m³) were unexpectedly associated with reduced risk (OR = 0.68, 95% CI: 0.58–0.8). This finding should be interpreted with caution and warrants further investigation; notably, the observed SO₂ levels were well below the World Health Organization air quality guideline values. CO exhibited an inverted U-shaped relationship with disease prevalence. Social participation functioned as a protective factor, lowering NCD risk (OR = 0.75, 95% CI: 0.66–0.84) and may partly explain the association between NO₂ exposure and NCDs. These findings highlight the complex and sometimes counterintuitive pathways through which air pollution and social participation jointly shape NCDs in later life. Policy interventions should integrate air quality improvements with initiatives that promote social participation to enhance resilience, reduce disparities, and foster healthy aging in polluted urban environments. For example, establishing well-ventilated indoor community centers equipped with air filtration systems in high-pollution areas could provide safer spaces for older adults to participate in social activities while minimizing exposure to harmful pollutants. Such interventions could simultaneously reduce environmental health risks and strengthen social participation, thereby offering a practical pathway for promoting healthy aging. Full article

Air quality affects large urban areas, where rapid urban development and human activities place constant pressure on ecosystems and public health. In this context, large-scale air quality assessment, supported by short-term forecasts, can provide useful information for environmental management and decision-making in urban areas, thus supporting evidence-based urban environmental management. The aim of this work is to design an affordable, smart real-time air pollution monitoring and prediction system for urban planning in overpopulated locations, which is deeply related to community health. The system focuses on real-time monitoring and forecasting of air quality. Prediction tasks were limited to gaseous pollutants CO and CO₂. Measurements were obtained over four months from a low-cost sensor platform installed in a highly populated neighborhood district in Baghdad, Iraq. Air quality prediction of gas concentrations was done using three types of time-series algorithms: Long Short-Term Memory, or LSTM; Gated Recurrent Unit, or GRU; and Temporal Convolutional Network, or TCN, models. Among these, the LSTM architecture showed more stable behavior and a higher predictive ${\mathrm{R}}^2$, ranging from 98.2% to 98.9%. Generally, the findings suggest that combining low-cost sensing technologies with artificial intelligence can offer a feasible and scalable solution for urban air quality monitoring. This approach may support cost-effective strategies for monitoring air quality in resource-constrained urban environments. Full article