Search Results (155)

Background: As a well-known environmental hazard, ambient fine particulate matter (PM_2.5, aerodynamic diameter ≤ 2.5 µm) has been positively correlated with an increased risk of digestive system diseases, including appendicitis, inflammatory bowel disease, and gastrointestinal cancer. Additionally, PM_2.5 exposure has been shown to alter microbiota composition and diversity in human and animal models. However, its impact on goblet cells and gut mucus barrier integrity remains unclear. Methods: To address this, 8-week-old male and female interleukin-10 knockout (IL10^−/−) mice, serving as a spontaneous colitis model, were exposed to concentrated ambient PM_2.5 or filtered air (FA) in a whole-body exposure system for 17 weeks. Colon tissues from the PM_2.5-exposed mice and LS174T goblet cells were analyzed using H&E staining, transmission electron microscopy (TEM), and transcriptomic profiling. Results: The average PM_2.5 concentration in the exposure chamber was 100.20 ± 13.79 µg/m³. PM_2.5 exposure in the IL10^−/− mice led to pronounced colon shortening, increased inflammatory infiltration, ragged villi brush borders, dense goblet cells with sparse enterocytes, and lipid droplet accumulation in mitochondria. Similar ultrastructure changes were exhibited in the LS174T goblet cells after PM_2.5 exposure. Transcriptomic analysis revealed a predominantly upregulated gene expression spectrum, indicating an overall enhancement rather than suppression of metabolic activity after PM_2.5 exposure. Integrated enrichment analyses, including GO, KEGG, and GSEA, showed enrichment in pathways related to oxidative stress, xenobiotic (exogenous compound) metabolism, and energy metabolism. METAFlux, a metabolic activity analysis, further substantiated that PM_2.5 exposure induces a shift in cellular energy metabolism preference and disrupts redox homeostasis. Conclusions: The findings of exacerbated gut barrier impairment and goblet cell dysfunction following PM_2.5 exposure provide new evidence of environmental factors contributing to colitis, highlighting new perspectives on its role in the pathogenesis of colitis. Full article

Fine particulate matter (PM_2.5) plays a major role in haze, and studying its spatio-temporal dynamics and influencing factors is crucial for improving air quality. However, previous studies have often obscured the spatio-temporal interactions of PM_2.5 and neglected local spatio-temporal differences in influencing factors. To address these limitations, this research utilized PM_2.5 concentration data derived from satellite remote sensing and employed exploratory spatio-temporal data analysis (ESTDA) methods to investigate the spatio-temporal evolution patterns of PM_2.5 in Chinese cities from 2000 to 2021. Furthermore, the effects of natural environmental and socioeconomic factors on PM_2.5 were analyzed from both global and local perspectives using a spatial econometric model and the geographically and temporally weighted regression (GTWR) model. Key findings include (1) The annual value of PM_2.5 from 2000 to 2021 ranged between 27.4 and 42.6 µg/m³, exhibiting a “bimodal” variation trend and phased evolutionary characteristics. Spatially, higher concentrations were observed in the central and eastern regions, as well as along the northwestern border, while lower concentrations were prevalent in other areas. (2) The spatial–temporal distribution of PM_2.5 was generally stable, demonstrating a strong spatial dependence during its growth process, with significant path dependence characteristics in local spatial clusters of PM_2.5. (3) Precipitation, temperature, wind speed, and the Normalized Difference Vegetation Index (NDVI) significantly reduced PM_2.5 levels, whereas relative humidity, per capita Gross Domestic Product (GDP), industrialization level, and energy consumption exerted positive effects. These factors exhibited distinct local spatio-temporal variations. These findings aim to provide scientific evidence for the implementation of coordinated regional efforts to reduce air pollution across China. Full article

In Turkish literature, hajj travelogues have been written since the 13th century, conveying Muslims’ experiences during the pilgrimage and explaining how to perform hajj. With the development and widespread use of the modern means of transportation in Türkiye from the 1940s onward, the pilgrims increasingly started to travel by air to avoid the hardships and duration of long journeys. However, this shift led to a decrease in visits to historical places along the traditional pilgrimage route from Türkiye to Mecca and Medina, consequently changing the content and nature of Hajj narratives. In spite of these changes, Mehmet Barut, a mufti (cleric), offered a unique response through his travelogue Hicaz Yolları [Hijaz Roads], which can be seen as a reaction to the rise in modern means of transportation. In 1965, Barut began his hajj journey from Tokat, within the border of the Republic of Türkiye, and travelled to Mecca and Medina by bus. Along the way, he visited Ankara, Konya, Tarsus, Iskenderun, Reyhanlı, Aleppo, Damascus, Jerusalem, Halilurrahman, Amman, Tabuk, Khaybar, and Medina before finally reaching Mecca. Barut’s travelogue is a contemporary non-fiction work, yet it was written in classical Turkish. In choosing to follow the historical pilgrimage route—established during the Ottoman period and beginning in Anatolia—Barut sought to revive and preserve the spiritual and cultural destinations and hajj journeys. His travelogue not only demonstrates his own travel experiences, but also reflects examples from the travelogue menazil-i hajj, offering insights into the historical significance of the cities and stopovers along the route. This study examines Hicaz Yolları from two key perspectives. First, it compares Barut’s chosen route with the historical Ottoman hajj route, highlighting key service areas and stopovers. Second, it explores the literary value of Barut’s work and its significance in contemporary Turkish literature. Ultimately, this study reveals that Barut’s travelogue not only kept the memory of traditional hajj pilgrimages alive, but also revived a fading tradition in an era dominated by modern means of transportation. Full article

Snow cover constitutes a medium that can be used as a way of assessing air pollution. The chemical composition of snow layers from the same snowfall event reflects the composition of atmospheric aerosols and dry precipitates, depending on the properties of the adsorbing surface and prevailing weather conditions. Analyzing snow samples provides reliable insights into anthropogenic pollution accumulated in soil and groundwater of different land use type areas, as well as allows the evaluation of the degree and sources of environmental pollution. The aim of the research was to determine the distribution of polycyclic aromatic hydrocarbons in various sites of Zawoja village and identify their possible sources and factors influencing their differentiation. A total of 15 surface snow samples of the same thickness and snowfall origin were collected from different locations in the village in the winter of 2024. The samples were pre-concentrated by solid phase extraction and analyzed by gas chromatography—tandem mass spectrometry. The sampling set was invented, and the extraction procedure and analysis parameters were optimized. A spatial distribution map of PAHs was created. The contamination of ∑16PAHs varied from 710 to 2310 ng/L in melted snow with the highest concentrations detected in Zawoja Markowa by the border of the Babia Góra National Park, which is interpreted mainly as a result of the topographical setting. Medium molecular weight PAHs were the dominant fraction, which, combined with specific PAH ratios, indicate the combustion of biomass and coal as the main source of contamination. Full article

Environmental justice research in the United States (US) documents greater air pollution exposures for Hispanic/Latino vs. non-Hispanic White groups. EJ research has not focused on the intersection of race/ethnicity and older age nor short-term fine particulate matter (PM_2.5) exposures. We address these knowledge gaps by studying US metropolitan area census tracts within 100 km of the US–Mexico border, a region with serious air quality issues. We use US Census American Community Survey data to construct sociodemographic variables and Environmental Protection Agency Downscaler data to construct long-term and short-term measures of PM_2.5 exposure. Using multivariable generalized estimating equations, we test for differences in PM_2.5 exposures between census tracts with higher vs. lower proportions of older Hispanic/Latino residents and older non-Hispanic White residents. The results indicate that as the proportion of the Hispanic/Latino population ≥ 65 years of age increases, long-term and short-term PM_2.5 exposures significantly increase. In contrast, as the proportion of the non-Hispanic White population ≥ 65 years of age increases, changes in long-term and short-term PM_2.5 exposures are statistically non-significant. These findings illuminate how race/ethnicity and older age intersect in shaping PM_2.5 exposure disparities and may inform efforts to mitigate air pollution exposures for overburdened people along the US–Mexico border. Full article

To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM₁₀ and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management. Full article

As a key initiative to integrate economic growth and green development in the era of the digital economy, the environmental effects of China’s Cross-border E-commerce Comprehensive Pilot Zone (CBEC-PZ) policy are not yet clear. Based on city-level data from 2014 to 2021 in China and leveraging the CBEC-PZ policy as a quasi-natural experiment, this study reveals that the CBEC-PZ policy has significantly enhanced local air quality, with particularly pronounced effects in eastern regions. While the policy did not degrade air quality in surrounding areas, spatial correlations of air quality levels among regions were observed due to atmospheric circulation dynamics. These findings underscore the importance of emphasizing regional coordination in green development within urban governance frameworks. The CEBC-PZ promotes the transformation of the energy structure and the improvement of air quality through reverse innovation, an ecological competitive advantage, and an agile governance mechanism. It is recommended to help synergize sustainable development and high-quality development in terms of strengthening reverse innovation and institutional innovation, expanding cross-regional synergistic governance, and deepening digital-real integration. Full article

The current examination used a multi-criteria decision-making (MCDM) approach to optimize the roughness parameters of S-shaped ribs (SSRs) in a solar thermal collector (STC) duct using air as the working fluid. Different SSRs were tested to identify the combination of parameters resulting in the best performance. Geometrical parameters such as relative roughness pitch ($P_R/e_{RH}$) varied from 4 to 12, relative roughness height ($e_{RH}/D_{hd}$) from 0.022 to 0.054, arc angle (${\alpha }_{Arc}$) from 30° to 75°, and relative roughness width ($W_{Duct}/w_{RS}$) from 1 to 4. The Nusselt number (${Nu}_{RP}$) and friction factor ($f_{RP}$), findings which impact the STC performance, rely on SSRs. The performance measurements show that no combination of SSR parameters lead to the best enhancement heat transfer rate at low enhancement in the friction. So, a hybrid multi-criteria decision-making strategy using the Analytical Hierarchy Process (AHP) for criterion significance and Multi Attributive Border Approximation Area Comparison (MABAC) for alternative ranking was used to determine which combination of geometrical parameters will result in the optimum performance of a roughened STC. This work employs a hybrid MCDM technique to optimise the effectiveness of an STC roughened with SSRs. To optimize the SSR design parameters, this study used the hybrid AHP-MABAC technique for analytical assessment of a roughened STC. The optimization results showed that the STC roughened with SSRs achieved the optimum performance at $P_R/e_{RH}$ = 8, $e_{RH}/D_{hd}$ = 0.043, ${\alpha }_{Arc}$ = 60° and $W_{Duct}/w_{RS}$ = 3. Full article

Amid global challenges like COVID-19 and trade wars, resilient logistics networks are crucial. The China Railway Express (CRE) offers a sustainable alternative to sea and air transport, supporting China’s national logistics strategy and strengthening links between China and Europe. This study applies a three-stage Social Network Analysis (SNA) to CRE using a “point–line–network” approach. It evaluates city logistics with the entropy weight method, modifies the gravity model to assess intercity logistical gravity, and constructs a weighted network to analyze centrality evolution through SNA. The results show that cities such as Zhengzhou, Wuhan, and Chongqing have emerged as central logistics hubs, benefiting from strategic investments in infrastructure and multimodal systems. However, regional disparities persist, with cities like Harbin, Lanzhou, and Urumqi facing challenges in integration due to infrastructure deficits and geographic constraints. Furthermore, inefficiencies in border logistics, inconsistent customs procedures, and limited multimodal integration hinder the CRE’s potential. Addressing these challenges through infrastructure investment, unified customs standards, multimodal hub development, and advanced technologies like IoT and blockchain is crucial for enhancing connectivity and competitiveness. The findings offer actionable recommendations for policymakers, logistics firms, and researchers, contributing to the sustainable optimization of the CRE within global supply chains. Full article

Exposure to traffic-related air pollution is not merely linked to respiratory health issues but also poses significant risks to cardiovascular well-being. Individuals from lower-income communities residing in high-pollution zones are particularly vulnerable to adverse cardiorespiratory health impacts. Pollutants such as fine particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), and ozone (O₃) are recognized as a leading, yet preventable, contributor to cardiorespiratory diseases. Although research has extensively explored the short-term impact of these pollutants on respiratory health, the immediate effects on cardiovascular outcomes require further study. We explored associations of traffic-related air pollutants with airway inflammation, lung function, and cardiovascular health outcomes (metabolic syndrome [MetS]) collected from a sample of low-income participants (N = 662) from a US–Mexico border county. Airway inflammation was measured using exhaled nitric oxide tests (eNO), while lung function parameters were measured by spirometry. MetS risk factors (waist circumference, blood pressure, triglycerides, HDL, and fasting blood glucose) were also measured. While spirometry measures were negatively associated with air pollutants (p < 0.05), no associations were noted for eNO. We also found positive associations in linear and logistic models between air pollutants and obesity (BMI: p < 0.04; waist: p < 0.03), fasting blood glucose (p < 0.03), and metabolic syndrome (p < 0.04). These findings reaffirm the immediate adverse effects of air pollution on respiratory function and shed light on its broader metabolic consequences. Environmental and neighborhood conditions could potentially influence the associations with obesity. At the same time, the links between fasting glucose and metabolic syndrome might indicate underlying oxidative stress and systemic inflammation. Full article

The objective of this paper is to propose an artificial neural network (ANN) model to forecast the Danube River temperature at Chiciu–Călărași, Romania, bordered by Romanian and Bulgarian ecological sites, and situated upstream of the Cernavoda nuclear power plant. Given the temperature increase trend, the potential of thermal pollution is rising, impacting aquatic and terrestrial ecosystems. The available data covered a period of eight years, between 2008 and 2015. Using as input data actual air and water temperatures, and discharge, as well as air temperature data provided by weather forecasts, the ANN model predicts the Danube water temperature one week in advance with a root mean square deviation (RMSE) of 0.954 °C for training and 0.803 °C for testing. The ANN uses the Levenberg–Marquardt feedforward backpropagation algorithm. This feature is useful for the irrigation systems and for the power plants in the area that use river water for different purposes. The results are encouraging for developing similar studies in other locations and extending the ANN model to include more parameters that can have a significant influence on water temperature. Full article

Thermopile sensor arrays provide a sufficient counterbalance between person detection and localization while preserving privacy through low resolution. The latter is especially important in the context of smart building automation applications. Current research has shown that there are two machine learning-based algorithms that are particularly prominent for general object detection: You Only Look Once (YOLOv5) and Detection Transformer (DETR). Over the course of this paper, both algorithms are adapted to localize people in 32 × 32-pixel thermal array images. The drawbacks in precision due to the sparse amount of labeled data were counteracted with a novel generative image generator (IIG). This generator creates synthetic thermal frames from the sparse amount of available labeled data. Multiple robustness tests were performed during the evaluation process to determine the overall usability of the aforementioned algorithms as well as the advantage of the image generator. Both algorithms provide a high mean average precision (mAP) exceeding 98%. They also prove to be robust against disturbances of warm air streams, sun radiation, the replacement of the sensor with an equal type sensor, new persons, cold objects, movements along the image frame border and people standing still. However, the precision decreases for persons wearing thick layers of clothes, such as winter clothing, or in scenarios where the number of present persons exceeds the number of persons the algorithm was trained on. In summary, both algorithms are suitable for detection and localization purposes, although YOLOv5m has the advantage in real-time image processing capabilities, accompanied by a smaller model size and slightly higher precision. Full article

The study of cross-border transport connectivity is significant for the development of regional integration and insight into global patterns. Comprehensive connectivity evaluations are lacking and insufficient attention has been paid to Latin American connectivity, so it is of great practical importance to comprehensively and rationally evaluate Latin American connectivity. In this article, based on the four modes of transport, namely, sea, road, air and railroad, and using the actual trade volume as a comparison, a connectivity evaluation index system with considerable reliability and generalization ability was constructed using the expert scoring method, QAP correlation analysis, QAP regression, and statistics, and the connectivity calculations of Latin America were obtained. Analyzing the connectivity structure of Latin America, it was found that cross-border passenger and cargo transport in the region was dominated by sea transport and supplemented by road and air transport, with railroads used the least. The overall connectivity of Latin America was low, and the overall development was unbalanced, with a strong law of spatial differentiation, which was mainly manifested in the strongest connectivity of the integrated coastal countries, followed by the island countries, and the lowest connectivity of the landlocked countries. Different countries assumed different roles in regional connectivity, which could be categorized into global hub type, local hub type and non-hub type based on the calculations. There was a spatial pattern of decreasing connectivity with distance in typical countries, but the rate of decline was closely related to their geographic location and the role they played in the connectivity network. This study can provide reference and inspiration for regional connectivity evaluation, improvement, and sustainable development. Full article

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity. Full article

In the context of water scarcity, understanding the mechanisms influencing and altering agricultural water consumption can offer valuable insights into the scientific management of limited water resources. Using Henan Province as a case study, this research applies the Mann–Kendall test method, the spatial Markov transfer chain model, the optimal parameter geo-detector model, and the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the evolution characteristics of agricultural water consumption in Henan Province and its key influencing factors. The findings revealed the following: (1) Agricultural water consumption has shown a significant decline from 1999 to 2022. (2) According to observations, the stability of agricultural water consumption exceeds the spillover effect, and cross-border grade transfer is challenging. Moreover, this phenomenon is influenced by the neighboring regions. (3) The key influencing factors of added agricultural value are the sown area of food crops, total sown area, irrigated area, and average annual air temperature. (4) Among the decomposition effects on agricultural water consumption, the contribution of each decomposition effect to changes in agricultural water consumption and the role of spatial distribution exhibit notable differences. Overall, these findings provide theoretical references for the efficient use of agricultural water resources and sustainable development in the region. Full article