Environmental Challenges最新文献

In this study, we established an air quality health index (AQHI) based on the associations between multiple air pollutants and respiratory and cardiovascular outpatient department (OPD) visits to communicate the health risks from air pollution in Bangkok, Thailand. The associations between various air pollutants, namely, suspended particulate matter (PM) with an aerodynamic diameter smaller than 2.5 µm and 10 µm (PM_2.5 and PM₁₀, respectively), sulphur dioxide (SO₂), and ozone (O₃) and the number of OPD visits for respiratory and cardiovascular diseases in Bangkok from 2016 to 2019 were assessed using generalised additive models with a Poisson link function. Significant associations were established between most cases of cardiovascular and respiratory diseases and these pollutants with a lag time of 0–7 days. The total excess risk was calculated to construct the AQHI, which was then adjusted to an arbitrary scale and banded into four groups based on the calculated score, where 1–3, 4–6, 7–10, and 10+ represented low risk, moderate risk, high risk, and very high risk, respectively. We found that the AQHI captured both high and very high risk levels during the day for most stations. The constructed AQHI also recorded a greater number of high and very high risk days than the currently used AQI but fewer than the WHO-based AQI. Our findings suggest that the AQHI can capture the combined effects of multiple air pollutants, which makes it an effective tool for communicating air pollution-related health risks.

Urbanization and agricultural activities are increasingly threatening karstic systems and the water resources they provide, which are crucial for the livelihood of rural communities. Over the past decade, the Río Grande de Comitán-Lagos de Montebello (RGC-LM) watershed in Chiapas, Mexico, has experienced significant deterioration in water quality. The objective of this study was to analyze the water quality dynamics within the RGC-LM watershed and their potential interaction with land use change. We conducted assessments of the physicochemical and microbiological parameters across the lotic and lentic systems within the watershed over eight monitoring campaigns (from 2013 to 2020). Cluster Analysis (CA) and Principal Component Analysis (PCA) statistical approaches were performed to identify the main drivers of surface water quality variability. This approach aims to enhance water quality assessments within environmental management strategies, with the goal of promoting sustainable land-use practices aimed at protecting and improving water resources within the watershed. Our findings indicate that there is a substantial difference between the upper and lower watershed water quality. The good water quality conditions observed in the higher altitude lakes (mountain lakes) can be attributed to favorable topographical features, specific land use patterns, and effective conservation measures. In contrast, the lotic system in the middle watershed and the lentic water bodies located at lower altitudes (plain lakes) exhibit signs of water quality degradation due to inefficient wastewater treatment and agricultural runoff. This study highlights the importance of implementing adequate environmental management strategies to address these water quality challenges effectively. Given the pressing issue of water quality degradation in karstic environments, we emphasize on the need for increased monitoring frequency of both physicochemical and microbiological parameters to capture seasonal variations and to further understand the vulnerability of water resources in karstic environments. Future assessment of nutrient concentrations, pesticide levels, and blue-green algae populations will also be crucial for evaluating the trophic state at a watershed scale.

This research seeks to understand the role of national innovation systems (NIS) in addressing countries’ environmental performance, namely their environmental health, ecosystem vitality, and climate change. The role played by NIS in these societal challenges was tested in 130 countries through cross-sectional models using the updated data from 2022/2021. The major findings of this research revealed that NIS contribute insignificantly to the attainment of meaningful environmental goals, such as enhancing ecosystem vitality or mitigating climate change. Despite making a positive impact towards protecting the population from environmental risks, NIS urgently need to make a paradigm shift towards environmental sustainability.

The two main issues facing Ethiopia's highlands are soil erosion and sedimentation. The purpose of this study was to evaluate the rate of soil loss and sediment yields in the Tullu Gana Watershed in the northwestern region of Ethiopia over the previous three decades (1990–2021). To create Rainfall Erosivity, Soil Erodibility, Slope Length and Steepness, Cover Management, and Conservation Practices, input data sets including rainfall, soil, Digital Elevation Model, land use, and land cover were utilized. Once more, the drainage area was used to determine the sediment delivery ratio. The RUSLE model and GIS were integrated in this study. The average soil loss rate was 1.01 tons ha⁻¹ yr⁻¹ in 1990, according to the results, but it increased to 43.80 tons ha⁻¹ yr⁻¹, 45.18 tons ha⁻¹ yr^−1, and 46.43 tons ha⁻¹ yr⁻¹ in 2000, 2010 and 2021, respectively. Between 1990, 2000, and 2010 to 2021, the average sediment yield in the study area increased from 0.19 tons ha⁻¹yr⁻¹, 8.41 tons ha⁻¹yr⁻¹, and 8.67 tons ha⁻¹yr⁻¹ to 20.61 tons ha⁻¹yr⁻¹, respectively. Over the past three decades, there has been an increase in agricultural land, plantations, and settlement, according to the results of changes in land cover. Grazing land, open forests, and shrub land, on the other hand, decreased during that time. In addition, the research area was divided into six sub-watersheds, with the aim of prioritizing them for conservation planning in the future, based on the average rate of soil loss. Among the six crucial sub watersheds, Sw2, Sw4, Sw5, and Sw6 were classified as having extremely high severity, while Sw1 was classified as having low severity. In general, determining erosion-prone areas and implementing sustainable land management depend on an understanding of the quantity of soil loss and sediment yield and the mapping of its distribution. These results are intended to assist decision-makers to prepare reliable plans for managing soil erosion while taking identified priority classes and hotspot areas into account.

Artificial intelligence (AI), unlike natural intelligence, possesses the ability to problem-solving activities by machines. As AI-based models increasingly provide robust approaches to predicting air pollution, they are becoming more widespread. Intelligent transportation systems (ITS) are poised to be significant solutions for sustainable mobility. These systems, by appropriately enhancing mobility, will prevent the concentration of air pollution in a region through transportation. This study aims to examine AI-based models used in air pollution prediction and demonstrate the effectiveness of intelligent transportation systems in improving transportation-related air pollution. As a sample region, Kocaeli Province, which has highly polluted air, the amounts of transportation-related NO_x pollutants emitted from light and heavy vehicles passing through the Dilovası district were modeled using Adaptive Neuro-Fuzzy (ANFIS) and Artificial Neural Networks (ANN). The results were compared with the outputs of the Calculations of Emissions from Road Transport (COPERT4) program. The evaluations revealed that ANFIS performed better in modeling NO_x pollutants. Based on the prediction results, in case of exceeding the NO_x limit, an intelligent transportation system redirecting vehicles to alternative routes was suggested. For the use of this system, scenarios proposing the redirection of cars in varying proportions, including single-plate, double-plate, and light vehicles, depending on route redirection, were proposed and evaluated. The evaluation of scenario results showed that redirecting a large number of cars to alternative routes with the assistance of ITS resulted in a significant decrease in emissions.

Since 2009, when a municipal veto right was established, wind power in Sweden has become intensely politicised and questioned. Having been considered the key technology to drive the energy system transition by all parliamentary parties before 2010, the 2022 elections brought into power a government that promised an end to the “steel forests of wind turbines”. Based on a comprehensive analysis of media coverage of wind power conflicts on national and local levels, the study explains these developments as a discursive struggle. It shows how localised and fragmented resistance groups evolved into generalised opposition to wind power per se. These shifts are explained as enabled by political, legal and discursive opportunity structures.

The study shows how political, legal and discursive opportunities allow for the scaling up of localised resistance to wind power. It explains wind power resistance as embedded in socio-political structures by analysing the relationship between resistance claims on different institutional and geographic scales. While previous research has identified a need to look beyond formal planning processes and at wider sociopolitical contexts to understand the formation of wind power resistance, there is a lack of comprehensive, longitudinal studies of how local resistance shift scales into national resistance. We provide a theoretically informed and empirically grounded analysis of a national case of growing wind power resistance. By showing the speed with which a counter discourse was able to shift scales and gain an entrenched position in Swedish energy politics, the paper enables an understanding of similar developments around Europe.

The blue economy (BE) development has recently grown in importance, but scholars have kept silent on their determinants, especially in developing countries. This article focuses on the role of green innovation (EI). Utilizing the R² decomposed linkage method, our investigation aims to delineate connections, particularly differentiating between contemporaneous and lagged linkages. This innovative approach is employed to examine the return transmission mechanism among the Ocean Health Index (OHI), green innovation (EI), nonrenewable energy (NONRENEW), and CO₂ emissions (CO2). The dynamic total linkage, subject to temporal variation, is contingent on economic events. Our findings emphasize the heightened influence of lagged linkage prevailing over contemporaneous linkage for all four variables. As observed before 2019, ocean health was acknowledged for its prominent role as a net receiver. Subsequently, ocean health was recognized as a net receiver in contemporaneous dynamics, while its significant impact as a net transmitter in lagged linkage persisted. The results have important policy implications for governments in promoting the use of green innovation to enhance blue health performance in Vietnam.

This study investigates ground subsidence in Maceió, the capital of Alagoas, Brazil, utilizing Sentinel-1 Synthetic Aperture Radar (SAR) data from November 2019 to December 2023. Ground subsidence poses significant risks to urban infrastructure and requires comprehensive monitoring and mitigation strategies. We employed the Interferometric Wide Swath (IW) mode of Sentinel-1A, acquiring high-resolution data in descending orbit with VV polarization. Through interferometric processing, including co-registration, phase unwrapping, and coherence estimation, we produced detailed subsidence maps for key neighborhoods such as Bebedouro, Farol, Mutange, Pinheiro, and Bom Parto. Our results reveal that subsidence in Maceió is highly variable across different neighborhoods, with cumulative subsidence reaching up to 3.83 meters in the most affected areas. The Bebedouro neighborhood, for instance, experienced subsidence up to 0.33 meters over an area of 41.85 hectares, while Farol saw significant ground movement impacting 59.49 hectares. The analysis indicates that high-density urban areas are particularly susceptible to subsidence, correlating with increased structural damage and flood vulnerability. The study underscores the importance of integrating geotechnical, urban planning, and environmental management strategies to address subsidence. We recommend the implementation of adaptive urban planning measures and enhanced monitoring techniques using satellite data to mitigate the impact of subsidence on infrastructure and communities. These findings provide a critical foundation for developing resilient urban environments in coastal cities prone to geological and anthropogenic subsidence.

Given the escalating apprehensions regarding climate change and environmental risks, the construction industry is confronted with the crucial task of guaranteeing the enduring structural robustness of buildings. Building Information Modeling (BIM) has emerged as an innovative tool to tackle these challenges effectively and enhance the performance of building structures processes by developing intelligent and sustainable structures. A comprehensive methodology was utilized to assess the relationships between the BIM factors and their impacts on smart and sustainable structure development by conducting a literature overview, quantitative analysis, exploratory factorial analysis (EFA), structural equation modeling (SEM), and predictive relevance findings. The findings show positive correlations between the implementation of BIM and the elements integral to smart and sustainable structure development. This study investigates the effects of BIM-driven environmental risk assessment, BIM-enabled energy efficiency, environmental performance, climate change adaptation strategies with BIM, and real-time monitoring and maintenance on the effectiveness of smart and sustainable structures development. The BIM-driven environmental risk assessment significantly impacts the sustainable and smart structures development with B = 0.360, followed by the real-time monitoring and maintenance with BIM with 0.316. This study provides the contemporary knowledge base by providing insights into BIM's transformative abilities in shifting sustainable and smart construction practices. The outcomes offer widespread insights to enterprise specialists interested in enhancing task effects and organizing sustainable and environmentally aware built surroundings. The growing popularity of BIM has implications for the future growth of the construction industry in phrases of ecologically smart and sustainable structure development.

Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals known for their widespread use in various industrial and consumer products. They enter the food chain via contaminated water, air, and soil, resulting in bioaccumulation in plants, fishes, foods, human milk, and blood serum. Here, we critically reviewed the literature published from 2005 to 2021 on the occurrence and distribution of Perfluorooctanoic acid (PFOA) and perfluoro-octane sulfonate (PFOS) as the most occurring PFAS in the aquatic environment in sub-Saharan Africa (SSA). To our knowledge, this is the first paper to review the status of PFAS in the SSA environment. This review found that almost all matrices studied in SSA regions have been polluted by PFAS with varying concentrations. This information suggests that the levels of PFAS in the environment deserve immediate attention. Furthermore, SSA faces unique challenges in understanding and managing PFAS contamination due to the scarcity of data in specific regions and the need for more administrative guidelines for monitoring PFAS in water. This review provides vital baseline information on the occurrences, distribution and contributing factors for their distribution in the SSA environment for better understanding to protect the environment and public health, and to develop sustainable solutions for the PFAS growing concern.