Nitrogen dioxide (NO2) is an ambient trace-gas result of both natural and anthropogenic processes. Long-term exposure to NO2 may cause a wide spectrum of severe health problems such as hypertension, ...diabetes, heart and cardiovascular diseases and even death. The objective of this study is to examine the relationship between long-term exposure to NO2 and coronavirus fatality. The Sentinel-5P is used for mapping the tropospheric NO2 distribution and the NCEP/NCAR reanalysis for evaluating the atmospheric capability to disperse the pollution. The spatial analysis has been conducted on a regional scale and combined with the number of death cases taken from 66 administrative regions in Italy, Spain, France and Germany. Results show that out of the 4443 fatality cases, 3487 (78%) were in five regions located in north Italy and central Spain. Additionally, the same five regions show the highest NO2 concentrations combined with downwards airflow which prevent an efficient dispersion of air pollution. These results indicate that the long-term exposure to this pollutant may be one of the most important contributors to fatality caused by the COVID-19 virus in these regions and maybe across the whole world.
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Environmental pollution has accelerated and intensified because of the acceleration of industrialization, therefore fabricating excellent materials to remove hazardous pollutants has become ...inevitable. MXenes as emerging transition metal nitrides, carbides or carbonitrides with high conductivity, hydrophilicity, excellent structural stability, and versatile surface chemistry, become ideal candidates for water purification and environmental remediation. Particularly, MXenes reveal excellent sorption capability and efficient reduction performance for various contaminants of wastewater. In this regard, a comprehensive understanding of the removal behaviors of MXene-based nanomaterials is necessary to explain how they remove various pollutants in water. The eliminate process of MXene-based nanomaterials is collectively influenced by the physicochemical properties of the materials themselves and the chemical properties of different contaminants. Therefore, in this review paper, the synthesis strategies and properties of MXene-based nanomaterials are briefly introduced. Then, the chemical properties, removal behaviors and interaction mechanisms of heavy metal ions, radionuclides, and organic pollutants by MXene-based nanomaterials are highlighted. The overview also emphasizes associated toxicity, secondary contamination, the challenges, and prospects of the MXene-based nanomaterials in the applications of water treatment. This review can supply valuable ideas for fabricating versatile MXene nanomaterials in eliminating water pollution.
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•The developmental timeline of MXenes for water treatment is summarized.•The synthesis strategies and characteristics of MXenes are introduced.•The removal behaviors of heavy metals, radionuclides, and organics are determined.•The removal mechanisms are electrostatic, coordination, and catalytic degradation.•MXenes with abundant terminal functional groups show excellent performance.
The continuous growth in overall energy demand and the related environmental impacts play a significant role in the large sustainable and green global energy transition. Moreover, the electrical ...power sector is a major source of carbon dioxide emissions. Therefore, renewable energy (RE) integration into the power grid has attracted significant economic, environmental, and technical attention in recent years. However, RE can also harm the environment, even though it is deemed less harmful than fossil fuel-based power. It may also cause technical, operational, and social issues. This, in return, more consideration and appropriate precautions should be taken. Given the recent sharp increase in RE utilization and its progressing impact on the world energy sector, evaluating its effect on the environment and sustainable development is limitedly explored and must be investigated. This study aims to discuss the role of RE integration in sustainable development. It provides an up-to-date review of the most recent global trend of various RE integrations into the power sector. The role and impact of this high integration level on the environment and the adverse effects of each RE source are discussed in detail. The recent challenges, including technical and operational challenges (i.e., voltage stability, frequency stability, and power quality), integration policy and standards challenges, RE environmental concerns, resource selection and location, and social challenges towards a sustainable electricity future and grid decarbonization, are comprehensively reviewed, discussed, and analyzed. A review of the literature was conducted from 2010 to 2021. Around 712 articles were classified during this process, and 177 papers were filtered for critical review. The literature analysis showed that RE integration has increased dramatically and has many benefits; however, more attention should be paid to mitigate its harmful impacts and recent challenges appeared. The new challenges resulting from the increasing generation of RE and linking it to the electric grid were listed to allow for future studies to find the appropriate solutions towards green and sustainable energy. Finally, towards a sustainable power system, the paper concludes with recommendations for future research directions.
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•Renewable energy towards sustainable electricity future is evaluated.•Present scenario/trend of renewables in power sector at the global level is presented.•Positive and negative environmental impacts of renewable integrated power sector are evaluated.•Recent challenges of renewables towards a sustainable and green power sector are analyzed.•Some recommendations for future research directions are presented.
This study examined the impact of disaggregate and aggregate energy, economic development, urbanization and political institutional quality on environmental pollution using a time series data ...spanning from 1971 to 2017. The study employed response surface regressions, structural break cumulative sum (CUSUM) test based on recursive residuals and ordinary least squares (OLS) residuals for parameter stability en route to estimating the autoregressive distributed lag (ARDL) regression. The environmental Kuznets curve (EKC) hypothesis is valid in South Africa with an extreme point of ZAR 56,114 which occurred in 2011. Evidence from the study reveals that political institutional quality plays a huge role in the social, governance and economic readiness to mitigate climate change and its impact. Structural adjustment in disaggregate and aggregate energy consumption, economic growth, and political institutional quality play a critical role in environmental quality. Fossil-fuel rich countries require diversification of the energy portfolio by incorporating renewable energy sources which will promote environmental sustainability and improve air quality while reducing their economy's vulnerability to price volatility. A paradigm shift from energy and carbon-intensive industries to a service-oriented economy will cause a structural economic change thus, aiding in the mitigation of climate change and its impacts.
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•The EKC hypothesis is valid in South Africa at a turning point of ZAR 56,114.•1% increase in fossil fuel will increase CO2 emissions by 10,436 kt in the long term.•1% increase in renewable energy decreases CO2 emissions by 2865 kt in the long-run.•Aggregate energy consumption and economic growth intensify environmental pollution.•Political institutional quality declines environmental pollution by 0.1% in the long-run.
Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to ...reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses.
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•The performance of meta-heuristics was assessed in flood susceptibility mapping.•ANFIS-PSO adopted faster convergence algorithm and outperformed other models.•ANFIS-PSO showed practical and robust results compared to other models.
COVID-19 pandemic is on a trajectory to cause catastrophic global upheaval with the potential to alter geopolitical and socio-economic norms. Many countries are frantically responding with staggering ...financial stimulus recovery initiatives. This opinion-paper reviews challenges, opportunities, and potential solutions for the post-COVID-19 era that focuses on intensive sustaining of agri-food supply chain in tandem with meeting the high demand for new green deal innovation. For example, the development of wet peatland innovation, known as Paludiculture, can intensively sustain and blend agri-food and green innovations that will help support COVID-19 pandemic transitioning. The future looks bright for the creation of new sustainability multi-actor innovation hubs that will support, connect, and enable businesses to recover and pivot beyond the COVID-19 pandemic. The nexus between first ‘Green Deal’ initiative supporting 64 selected European Startups and SMEs (European Innovation Council) and 43 Irish Disruptive Technology projects are addressed in the context of cross-cutting developments and relevance to COVID-19. Candidate areas for future consideration will focus on climate action, digitization, manufacturing, and sustainable food production, security, and waste mitigation. Recommendations are also provided to facilitate community transitioning, training, enterprise, and employment to low carbon economy.
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•COVID-19 pandemic presents opportunities for sustainable agri-food production and to accelerate green innovation.•Sensible yet ambitious technical-economical recovery plans are urgently needed when countries reopen.•COVID-19 may create disruptive technologies that cross-cuts agri-food, ICT, health, and environment.•Multi-agency converging innovation hubs have the potential to accelerate socio-economic recovery.
The rapid increase in steel slag generation globally highlights the urgent need to manage the disposal or utilization processes. In addition to conventional landfill disposal, researchers have ...successfully reused steel slag in the construction, chemical, and agricultural fields. With the large portions of alkaline silicate mineral content, steel slag can also be used as a suitable material for carbon capture to mitigate global warming. This article comprehensively reviews the environmental performance of steel slag utilization, especially emphasizing quantitative evaluation using life cycle assessment. This paper first illustrates the production processes, properties, and applications of steel slag, and then summarizes the key findings of the environmental benefits for steel slag utilization using life cycle assessment from the reviewed literature. This paper also identifies the limitations of quantifying the environmental benefits using life cycle assessment. The results indicate steel slag is largely utilized in pavement concrete and/or block as a substitution for natural aggregates. The associated environmental benefits are mostly attributed to the avoidance of the large amount of cement utilized. The environmental benefits for the substitution of traditional energy-intensive material and carbonation treatment are further discussed in detail. Due to the presence of heavy metals, the potential risks to human and ecological health caused by the manufacturing process and usage stage are examined. Finally, the current challenges and global social implications for steel slag valorization are summarized.
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•System expansion and allocation choices may lead to opposite interpretations.•Environmental benefit caused by replacement of steel slag aggregate is limited.•Mitigation of climate change is easy to be achieved by reducing cement addition.•Carbonation method should be optimized to maximize the carbon capture benefit.
There is a growing pressure of human activities on natural habitats, which leads to biodiversity losses. To mitigate the impact of human activities, environmental policies are developed and ...implemented, but their effects are commonly not well understood because of the lack of tools to predict the effects of conservation policies on habitat quality and/or diversity. We present a straightforward model for the simultaneous assessment of terrestrial and aquatic habitat quality in river basins as a function of land use and anthropogenic threats to habitat that could be applied under different management scenarios to help understand the trade-offs of conservation actions. We modify the InVEST model for the assessment of terrestrial habitat quality and extend it to freshwater habitats. We assess the reliability of the model in a severely impaired basin by comparing modeled results to observed terrestrial and aquatic biodiversity data. Estimated habitat quality is significantly correlated with observed terrestrial vascular plant richness (R2=0.76) and diversity of aquatic macroinvertebrates (R2=0.34), as well as with ecosystem functions such as in-stream phosphorus retention (R2=0.45). After that, we analyze different scenarios to assess the suitability of the model to inform changes in habitat quality under different conservation strategies. We believe that the developed model can be useful to assess potential levels of biodiversity, and to support conservation planning given its capacity to forecast the effects of management actions in river basins.
•We present a model for the simultaneous assessment of land & aquatic habitat quality.•We assess the reliability of the model as a proxy for biodiversity in river basins.•We demonstrate the suitability of the model for scenario analysis in river basins.•We recommend the model to assess biodiversity changes of conservation planning actions.
Beijing's air pollution has become of increasing concern in recent years. The central and municipal governments have issued a series of laws, regulations, and strategies to improve ambient air ...quality. The “Clean Air Action” issued in 2013 and the “Comprehensive Action” issued in 2017 largely addressed this concern. In this study, we assessed the effectiveness of the two action plans by environmental monitoring data and evaluated the influencing factors including meteorology, pollutant emissions, and energy structure. The spatial distributions of air pollutants were analyzed using the Kriging interpolation method. The Principal Component Analysis-Multiple Nonlinear Regression (PCA-MNLR) model was applied to estimate the effects of meteorological factors. The results have shown that Beijing's air quality had a measurable improvement over 2013–2019. “Good air quality” days had the highest increases, and “hazardous air quality” days had the most decreases. SO2 decreased most, followed by CO, PM2.5, PM10, and NO2 in descending order, but O3 showed a fluctuant increase. The “Comprehensive Action” was more effective than the “Clean Air Action” in reducing heavy pollution days during the heating period. The meteorological normalized values of the main pollutants were lower than the observation data during 2013–2016. However, the observed values became lower than the normalized values after 2017, which indicated beneficial weather conditions in 2017 and afterwards. The emissions of SO2 and dust significantly decreased while NOx had a slight decrease, and the energy structure changed with a dramatic decrease in coal consumption and an obvious increase in the use of natural gas and electricity. The significant reduction of coal-fired emissions played a dominant role in improving Beijing's air quality, and vehicle emission control should be further enhanced. The results demonstrated the effectiveness of the two action plans and the experience in Beijing should have potential implications for other areas and nations suffering from severe air pollution.
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•The air quality of Beijing before and after two action plans was assessed.•SO2 decreased the most, followed by CO, PM2.5, PM10, and NO2, while O3 increased slightly.•The control of coal consumption played a dominant role in pollutant reduction.•The influences from meteorology, pollutant emissions, and energy structure were evaluated.•The control measures have proved to be effective in improving Beijing's air quality.
Capsule: Neighbourhood greenness may benefit mental health by decreasing air pollution.
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•NDVI and streetscape greenery (SVG) were used to assess greenness exposure, and trees ...(SVG-tree) and grasses (SVG-grass) were distinguished.•Both objective (PM2.5 and NO2) and subjective (perceived air pollution) measures were used to quantify air pollution exposure.•NDVI, SVG-tree and SVG-grass were positively associated with psychological well-being.•The SVG-mental health association was mediated by ambient PM2.5, NO2 and perceived air pollution in parallel mediation models.•The SVG-mental health association was mediated by ambient PM2.5-perceived air pollution and NO2-perceived air pollution in serial mediation models.•Neither measures of air pollution mediated the association between NDVI and psychological well-being.
China’s rapid urbanization has led to an increasing level of exposure to air pollution and a decreasing level of exposure to vegetation among urban populations. Both trends may pose threats to psychological well-being. Previous studies on the interrelationships among greenness, air pollution and psychological well-being rely on exposure measures from remote sensing data, which may fail to accurately capture how people perceive vegetation on the ground. To address this research gap, this study aimed to explore relationships among neighbourhood greenness, air pollution exposure and psychological well-being, using survey data on 1029 adults residing in 35 neighbourhoods in Guangzhou, China. We used the Normalized Difference Vegetation Index (NDVI) and streetscape greenery (SVG) to assess greenery exposure at the neighbourhood level, and we distinguished between trees (SVG-tree) and grasses (SVG-grass) when generating streetscape greenery exposure metrics. We used two objective (PM2.5 and NO2 concentrations) measures and one subjective (perceived air pollution) measure to quantify air pollution exposure. We quantified psychological well-being using the World Health Organization Well-Being Index (WHO-5). Results from multilevel structural equation models (SEM) showed that, for parallel mediation models, while the association between SVG-grass and psychological well-being was completely mediated by perceived air pollution and NO2, the relationship between SVG-tree and psychological well-being was completely mediated by ambient PM2.5, NO2 and perceived air pollution. None of three air pollution indicators mediated the association between psychological well-being and NDVI. For serial mediation models, measures of air pollution did not mediate the relationship between NDVI and psychological well-being. While the linkage between SVG-grass and psychological well-being scores was partially mediated by NO2-perceived air pollution, SVG-tree was partially mediated by both ambient PM2.5-perceived air pollution and NO2-perceived air pollution. Our results suggest that street trees may be more related to lower air pollution levels and better mental health than grasses are.
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