Introduction
In recent years, violent extremism (VE) attacks have escalated worldwide. More schools and students are being attacked. Examining and addressing VE core causes through preventing VE ...(PVE) strategies can help avoid future atrocities. Due to the tremendous proliferation of research geared toward PVE, an extensive but disorganized knowledge review has accumulated in recent years. The review aims to discover several common themes and strategies across different disciplines and suggests resilience approaches might be the effective framework for PVE worldwide.
Methods
This study followed the guidelines provided by PRISMA. A systematic literature review on 81 articles was conducted in January 2022, with a screening approach starting from the title, and finally, full articles.
Results
Seventeen studies were identified with a total sample of 2415 vulnerable young adults, age range: 16–29, male: 68.65% and female 31.35% mainly influenced VE pursuits through internet, TV and social media. In addition, the study identified that for PVE, individual actions would include ineffective approaches compared to a group approach starting from family to educational institutions.
Conclusions
The effective PVE will be ensured by developing strategies for resilient individuals and dialoguing from the social‐ecological perspective for taking practical actions in reducing VE activities.
The consequences of droughts are far-reaching, impacting the natural environment, water quality, public health, and accelerating economic losses. Applications of remote sensing techniques using ...satellite imageries can play an influential role in identifying drought severity (DS) and impacts for a broader range of areas. The Barind Tract (BT) is a region of Bangladesh located northwest of the country and considered one of the hottest, semi-arid, and drought-prone regions. This study aims to assess and predict the drought vulnerability over BT using Landsat satellite images from 1996 to 2031. Several indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Soil Moisture Content (SMC), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). VHI has been used to identify and predict DS based on VCI and TCI characteristics for 2026 and 2031 using Cellular Automata (CA)-Artificial Neural Network (ANN) algorithms. Results suggest an increasing patterns of DS accelerated by the reduction of healthy vegetation (19 %) and surface water bodies (26 %) and increased higher temperature (>5 °C) from 1996 to 2021. In addition, the VHI result signifies a massive increase in extreme drought conditions from 1996 (2 %) to 2021 (7 %). The DS prediction witnessed a possible expansion in extreme and severe drought conditions in 2026 (15 % and 13 %) and 2031 (18 % and 24 %). Understanding the possible impacts of drought will allow planners and decision-makers to initiate mitigating measures for enhancing the communities preparedness to cope with drought vulnerability.
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•Agricultural drought assessment was conducted by applying VCI, TCI, and VHI.•CA-ANN algorithms were used to predict future drought severity (DS).•DS will likely occur towards NW, SW, and SE directions in predicted years.•Climate change, agricultural practices, and land cover changes might be the possible reason for DS.
Human pressures, combined with changing hydrology and land resources, have a distinct effect on the carbon chain and ecosystem resilience. The increase in urban areas contributes significantly to the ...loss of vegetation cover (VC), which accelerates carbon emissions, increasing land surface temperature (LST) and global warming. This study used remote sensing and GIS techniques to estimate the Land Use/Land Cover (LU/LC) changes by focusing on VC loss and its impact on LST and carbon emissions over Cumilla during 1994–2019. The study's findings confirmed an alarming reduction of VC by 6.65% from 1994 to 2009 with around 20% increase of urban area, contributing LST rise from 23 °C to 31 °C. The trends were continuous, with a decrease in VC loss by 1.75% during 2009–2019, contributing 28 °C–36 °C LST rise in the study area. The results also confirmed a significant positive correlation between VC loss and LST. Results indicate that the massive amount of carbon attracted the sun's rays due to the VC loss and raising the surface temperature by 11.2 °C (1.86 °C/year) since 1994, which directly contributing to global warming. Thus, to mitigate climate hazards, efforts to slow urbanization to reduce pollution gateways and increase carbon sinks through afforestation will significantly contribute to protecting humanity from global warming.
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•Vegetation cover has been lost by 9% in 25 years.•Vegetion cover loss increase average temperature by 11 °C in 25 years.•Maximum temperature recorded in industrial zone of the study area.•Very healthy vegetation radiate low temperature where poor vegetation recorded high.
In response to changes in climatic patterns, a profound comprehension of air pollutants (AP) variability is vital for enhancing climate models and facilitating informed decision-making in nations ...susceptible to climate change. Earlier research primarily depended on limited models, potentially neglecting intricate relationships and not fully encapsulating associations. This study, in contrast, probed the spatiotemporal variability of airborne particles (CO, CH4, SO2, and NO2) under varying climatic conditions within a climate-sensitive nation, utilizing multiple regression models. Spatial and seasonal AP data were acquired via the Google Earth Engine platform, which indicated elevated AP concentrations in primarily urban areas. Remarkably, the average airborne particle levels were lower in 2020 than in 2019, though they escalated during winter. The study employed linear regression, Pearson's correlation (PC), Spearman rank correlation models, and Geographically Weighted Regression (GWR) models to probe the relationship between pollutant variability and climatic elements such as rainfall, temperature, and humidity. Across all seasons, APs showed a negative correlation with rainfall while displaying positive correlations with temperature and humidity. The GWR and PC models produced the most reliable results from all the models employed, with the GWR model superseding the rest. Moreover, heightened aerosol levels were detected within a rainfall range of 600 mm/season, a temperature range of 25–30 °C, and humidity levels of 75 %–85 %. Overall, this study emphasizes the growing levels of APs in correlation with meteorological changes. By adopting a comprehensive approach and considering multiple factors, this research provides a more sophisticated understanding of the relationship between AP variability and climatic shifts.
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•Air pollutants peaked at 600 mm rainfall, 25–30 °C temp, 75–85 % humidity.•Temperature and Humidity positively correlated with CH4, NO2, and SO2 levels.•Rainfall showed a consistent negative correlation with GHGs across all seasons.•Rapid urbanization and climate change may worsen Bangladesh's air quality.•Mitigation strategies needed to curb air pollution amid climate change.
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•Built-up areas increased by 388%,replacing 37% of green cover and water bodies.•Loss of 21% green cover reduced annual average carbon sequestration by 21.65 × 106 Mg.•Cropland ...increased annual average carbon sequestration by 6154163.85 Mg.•Elevation, vegetation health, and precipitation influence carbon storage.
Forests are vital in combating climate change by storing and sequestrating CO2 from the atmosphere. Measuring the influence of land use/land cover (LULC) changes on the capacity of carbon storage (CS) within forest ecosystems presents a significant challenge. This study employs remote sensing techniques to examine the changes in spatiotemporal patterns of CS in the Chittagong Hill Tracts (CHT), resulting from LULC alterations between 1996 and 2021. LULC change patterns were identified for six different years utilizing the Google Earth Engine (GEE). The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was combined with GEE to evaluate the changing patterns of CS. The study discovered that the CHT region experienced a loss of 21.65 × 106 Mg of CS, owing to a 21% reduction in vegetation cover (2862.85 km^2) during the study period. The central city area (Chittagong) accounted for the most significant loss of CS (7.99 × 106 Mg), while the suburban areas of Khagrachari (0.92 × 106 Mg) and Rangamati (3.53 × 106 Mg) contributed the least. The multiple regression model revealed that elevation and vegetation characteristics significantly influenced CS. The findings underscore the importance of developing policies and strategies that mitigate the adverse effects of land cover change on CS, and advocate for sustainable forest management practices that strike a balance between ecological, social, and economic concerns.
Changes in land use/land cover (LULC) and land surface temperatures (LST) contribute significantly to the formation and intensity of urban heat islands (UHI) effects. The urban thermal field variance ...index (UTFVI) can effectively describe any city's UHI (thermal characteristics) effect. This study aims to assess and predict the seasonal (summer and winter) UTFVI scenario to evaluate the thermal characteristics of Sylhet city, Bangladesh. Landsat 4–5 TM and 8 OLI images from 1995 to 2020 were used to assess the previous status of LULC and UTFVI and predict the future changes for 2025 and 2030 using cellular automata and artificial neural network machine learning algorithms. Prediction results indicate a substantial increase in urban built-up areas by 42% and 44% in 2025 and 2035, followed by reductions in green cover (21% and 22%), bare land (20% and 21%) and water bodies (1%). The rapid expansion of built-up areas will lead to 13 km2 and 14 km2 stronger UTFVI zones in the predicted years. The study provides effective strategies for mitigating the UTFVI effects by avoiding dense infrastructural development, increasing plantation and water bodies, rooftop gardening and using white colour roofs in construction. The findings of this study will allow the urban planners, policymakers and local government to ensure an eco-friendly, inclusive and sustainable urban development through functional modification and replacement of the LULC distribution depending on the present and future circumstances.
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•Directional changes of LULC and seasonal UTFVI shift in Sylhet city were analyzed.•Reduction of green cover significantly increase UTFVI effect.•LULC vs UTFVI relationship better explain impacts of different land cover on thermal environment.•Seasonal UTFVI prediction exhibit a gradual decrease in overall thermal characteristics.•Predicted UTFVI vs LULC demonstrated the highest UTFVI concentration in the built-up area.
The impact of the rapid expansion of urban land on the urban thermal environment and carbon chain has attracted widespread attention. This paper uses artificial neural network-cellular automata ...(ANN-CA) and long short-term memory model of the improved whale optimization algorithm (IWOA-LSTM) models to predict the changes of LULC and LST, and explores the correlation between LST and carbon emissions in Wuhan.The results show that urban land will occupy >70.05% of the central urban area, while green land and water areas will continue to decrease to varying degrees in 2030 and 2040. The area of the high temperature area (LST > 30 °C) is expanding in the urban land, while the green land and the low temperature area of the water body are gradually shrinking. The area of high temperature is expanding, and the area with LST > 30 °C accounts for 67.84% in summer, and the area with LST at 10 °C ∼ 15 °C accounts for 96.32% in winter. The fitting results of correlation regression show that there is a significant correlation between carbon emissions and LST. The R2 of linear fitting between LST and carbon emissions in summer and winter of 2000 are 0.6227 and 0.6143, respectively. The R2 of linear fitting in summer and winter is higher, both of which are >0.85 in 2010 and 2020. This research may provide new clues for future urban development and thermal environment governance and carbon emission mitigation.
•The IWOA-LSTM model is used to predict the seasonal changes of LST.•The ANN-CA model is used to predict the change of LULC.•Analyze the changes of LULC and LST in different periods.•Explore the correlation between LST and carbon emissions.
Worldwide the COVID-19 pandemic has accelerated sufferings of mental health and behaviour attitudes of people. Many countries, including Bangladesh, reported suicide as extreme consequences of the ...psychological burden influenced by COVID-19. The present study explores human stress and its factor influenced by COVID-19 in Bangladesh, which significantly affect the quality of life.
An online-based questionnaire survey was conducted among 651 adult Bangladeshi populations by capturing socio-demographic information, possible human stress, and consequences of the pandemic. A set of statistical tools such as Pearson's Correlation Matrix (PCM), T-test, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were applied to identify the relationship between different factors and influential factors increasing human stress.
More than 83% of the participants are facing COVID-19 related mental stress, which results in short temper, sleep disorder, and family chaos. PCA and HCA outcomes indicated a significant relationship between the respondents' opinions and human stress factors, which harmonized with the country's existing scenario. PCM results enlighten the relationship between human stress factors and found financial hardship, cutting back daily spending, and food crisis are interconnected together causes stress. Also, hampering students' formal education and future career plans significantly contribute to mental stress.
Based on the above findings, it's crucial to introduce a time-oriented strategy and implement precaution monitoring plans for Bangladesh. The rescue plan will help people to manage the pandemic and improve mental health to fight against psychological challenges related to COVID-19 and future pandemics.
•COVID-19 create significant mental and economic stress for Bangladeshi people.•Economic crisis due to pandemic is strongly correlated with losing job.•Hampering formal eduction due to COVID-19 linked with future career prospects.•Mental stress-induced by COVID 19 hamper sound sleep and reduce work efficiency.
Urbanization causes enormous land use and land cover (LULC) changes, which creates a significant impacts on land surface temperature (LST) in rapidly growing mega-cities. The substantial increment of ...the LST creates urban heat island (UHI) effects in cities. This study first identified the pattern of the LULC changes, and later, investigated their impacts on LST in Rajshahi City Corporation (RCC) areas for the years of 1999, 2009 and 2019 using Landsat TM/OLI satellite images. This study explored the impact of LULC change on LST through LST distribution in different land use categories, cross-section profile of LULC wise LST variability, and a correlation between LULC indexes (NDVI, NDBI, NDBaI & NDWI) and LST. The Multi-Layer Perceptron-Markov Chain (MLP-MC) and Artificial Neural Network (ANN) methods were utilized to simulate the LULC and LST maps, respectively, for the years of 2029 and 2039. The accuracy of LULC and LST simulation models are more than 85% and 90% , respectively, based on the validation results. Simulation results show if the current trend of urban growth continues, 70% and 88% of RCC area will experience temperature more than 38 °C in 2029 and 2039, respectively. Such impacts need to be considered and evaluated immediately for ensuring sustainable urbanization and natural resource management in the RCC area. This study will be helpful for urban planners and environmental engineers to understand the impacts of LULC change (e.g. loss of vegetation cover, agricultural land and water bodies to accommodate extensive urban growth) on LST and to propose appropriate policy measures to control it.
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•Patterns of urban expansion and LST shift in different directions of Rajshahi were analyzed.•Variations of LULC wise LST patterns were depicted.•The cross-section profile better explains the relationship between LULC and LST.•Optimizing in LULC and LST configuration help to mitigate the urban heat island.•Simulation of LST and LULC approach needs to integrate into the city master plan.
Municipal solid waste (MSW) management has been a growing problem in fast-developing cities. A considerable amount of solid waste is generated daily and disposed anywhere, which creates an unhealthy ...environment. This study aims to develop a model to determine household solid waste (HSW) generation using multiple linear regression and identify suitable landfill sites to ensure proper MSW disposal in Rangpur City, Bangladesh. Socioeconomic variables data like average monthly income, educational level, family size, age of family head, and average HSW generation per day were collected from 381 respondents through stratified random sampling with a 95% confidence level. Multi-criteria decision analysis (MCDA) was performed using variables like surface water, slope, road network, and land use through GIS and remote sensing to find suitable landfill sites. Results of the model show no multicollinearity as the variance inflation factor was estimated to be less than 2 for each independent variable. Furthermore, the model provides a moderate overall fit because of the coefficient of determination (
R
2
= 0.661), which denotes the independent variables’ predictive capability. The results also demonstrate that family size and education are the most critical variables in predicting waste generation because of the values of coefficients 122.39 and − 184.72, respectively. This study also illustrated suitable landfill sites through MCDA, which can be a useful resource for the city authority to ensure environmental sustainability by implementing effective strategies for proper MSW management.
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