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Abstract Background Urban heat island (UHI) is an urban climate phenomenon that primarily responds to urban conditions and land use change. The extent of hard surfaces significantly influences the thermal properties of the land. To address this issue, a novel approach quantifying the association between land use and UHI is developed. This study offers a new technique for effectively estimating the effect of land use on the UHI intensity using the combination of urban heat intensity index (UHII) and land contribution index (LCI) derived from Landsat 8 OLI images. The time-series thermal effect of land use on the UHI intensity can be determined according to the ratio in mean temperature between specific land use and the whole study site. The study was conducted in the Hulu Langat district, Malaysia during 2014–2021. Results The UHI intensity rose from 0.19 in 2014 to 0.70 in 2021. The negative value of LCI for vegetation areas and water bodies obtained its negative contribution to the urban heat island, while the positive value of LCI for bare areas and built-up areas showed its positive effect on the urban heat island. The LCI value for urban areas showed a significant increase in the 7 years such as 0.51, 0.66, 0.69, and 0.75 for periods 2014, 2016, 2018, and 2021, respectively. The change in LCI from 2014 to 2021 for the transformation of bare area and forest was recorded to be 0.23 and − 0.02, respectively. Thus, the conversion of forests into urban areas had a negative effect on the increment of UHI intensity. Conclusions Overall, these findings are useful for policy-making agency in developing an effective policy for reducing high UHI intensity and planning long-term land use management.

Abstract The hydrological availability and scarcity of water can be affected by geomorphological processes occurring within a watershed. Hence, it is crucial to perform a quantitative evaluation of the watershed’s geometry to determine the impact of such processes on its hydrology. Geographic information systems (GIS) and remote sensing (RS) techniques have become increasingly significant because they enable decision-makers and strategists to make accurate and efficient decisions. To prioritize sub-watersheds within the Wyra watershed, this research employs two methods: morphometric analysis and hypsometric analysis. The watershed was divided into eleven sub-watersheds (SWs). The prioritization of sub-watersheds in the Wyra watershed involved assessing several morphometric parameters, such as relief, linear, and areal features, for each sub-watershed. Furthermore, the importance of the sub-watersheds was determined by computing hypsometric integral (HI) values using the elevation–relief ratio method. The final prioritization of sub-watersheds based on morphometric analysis was determined through the integration of principal component analysis (PCA) and weighted sum approach (WSA). SW2 and SW9 have had higher priorities using morphometric analysis, whereas SW6, SW7, and SW10 have obtained higher priorities using hypsometric analysis. SW4 is the most common SW that shares the same priority. The most vulnerable sub-watersheds are those with the highest priority, and therefore, programmes for soil and water conservation should pay more attention to them. The conclusions of the study may prove useful to various stakeholders involved in initiatives related to watershed development and management.

Abstract Despite Bangladesh being vulnerable to cyclones, there is a dearth of research on cyclone vulnerability assessment. Assessing a household's vulnerability is considered a crucial step in avoiding the adverse effects of catastrophe risks. This research was conducted in the cyclone-prone district of Barguna, Bangladesh. This study's purpose is to evaluate this region's vulnerability. A questionnaire survey was conducted using a convenience sample technique. A door-to-door survey of 388 households in two Unions of Patharghata Upazila, Barguna district, was conducted. Forty-three indicators were selected to assess cyclone vulnerability. The results were quantified using an index-based methodology with a standardized scoring method. Where applicable, descriptive statistics have been obtained. In terms of vulnerability indicators, we also utilized the chi-square test to compare Kalmegha and Patharghata Union. When appropriate, the non-parametric Mann–Whitney U test was employed to evaluate the relationship between the Vulnerability Index Score (VIS) and the union. According to the results, the environmental vulnerability (0.53 ± 0.17) and the composite vulnerability index (0.50 ± 0.08) were significantly greater in Kalmegha Union than in Patharghata Union. They faced inequity in government assistance (71%) and humanitarian aid (45%) from national and international organizations. However, 83% of them underwent evacuation practices. 39% were satisfied with the WASH conditions at the cyclone shelter, whereas around half were dissatisfied with the status of the medical facilities. Most of them (96%) rely only on surface water for drinking. National and international organizations should have a comprehensive plan for disaster risk reduction that encompasses all individuals, regardless of race, geography, or ethnicity.

Abstract This study aims to assess people’s preparedness for a potential earthquake in Dhaka, the capital of Bangladesh. We have employed a model with six dimensions of holistic individual preparedness. A self-reported online survey included 677 total participants. The multiple linear regression model and the Spearman rank correlation were used as needed. The majority of the participants (> 65%) did not have experience with any earthquake preparedness program, despite the fact that 92% of the population surveyed claimed to have experienced an earthquake in their region. More than 50% of those who experienced earthquakes acquired knowledge. 30% of people do not have access to immediate financial support in the event of a crisis. It was estimated that almost 50% of the population did not have earthquake insurance. Females lack the adaptability of males. A person’s level of earthquake preparedness was significantly associated with their level of education, household head occupation and monthly income, type of residential unit, and experience of earthquake preparedness program. Therefore, these factors should be considered while figuring out how to better prepare for earthquakes. A combination of holistic earthquake preparedness programs and effective education is generally required for competent holistic earthquake preparedness.

Abstract Due to rapid population growth and the limitation of land resources, the sustainability of agricultural ecosystems has attracted more attention all over the world. Human activities will alter the components of the atmosphere and lead to climate change, which consequently affects crop production badly. In this context, wheat is considered an important crop and ranks as one of the top strategic crops globally. The main objective of this research is to develop a new approach (a weighted climatic suitability index) for evaluating the climate suitability for wheat production. The specific objectives are to project the impact of future climate change on wheat suitability using three models based on WCSI and CMIP6-based projections and to identify the most vulnerable area to climate change and productivity reduction. The climatic criteria for wheat production were selected and classified into eight indicators based on the Sys' scheme and the FAO framework, and then the weighted overlay approach was used in conjunction with the analytic hierarchy process. To confirm the reliability of the integrated WCSI, we determined the nonlinear curve fitting of integrated WCSI-induced wheat yields by the exponential growth equation. Finally, the CMIP6-GCMs projected from three shared socioeconomic pathways were used for WCSI mapping and predicting wheat yields in the short and long term (Southern Syria was selected as a case study). The results show that the nonlinear correlation between wheat yields and the integrated WCSI was 0.78 (R2 = 0.61) confirming the integrated WCSI's reliability in reflecting yield variation caused by climate suitability. The results indicated that WCSI for wheat will be lower over the study area during 2080–2100 compared to the current climate. During 2080–2100, the wheat yield is projected to decrease by 0.2–0.8 t. ha−1 in the western parts of the study area. The findings of this study could be used to plan and develop adaptation strategies for sustainable wheat production in the face of projected climate change. The results of the study will also help in the strategic planning of wheat production in Syria under the projected climate. The results of this research are limited to small areas as a case study, although they are not relevant to similar regions worldwide. However, the study employs novel analytical methods that can be used broadly.

AbstractLandslides rank as the third most common natural disaster globally, and the Indian Himalaya Region is no exception, experiencing severe impacts during the rainy season. This study focuses on creating a comparative landslide susceptibility map for the West Sikkim district in India using probabilistic and heuristic approaches. The frequency ratio (FR) and information value (IV) methods are employed for the probabilistic approach, while the analytic hierarchy process (AHP) is used for the heuristic approach. Eleven factors are considered in the analysis. The resulting landslide susceptibility (LS) map demonstrates accuracies of 77% for FR, 74% for IV, and 57% for AHP methods. Preliminary qualitative risk assessment is conducted, incorporating building and population density, as population and buildings are the most vulnerable elements in the society. The LS map with the highest accuracy (from FR) serves as the landslide potential factor, combined with building and population density as the risk damage potential factors for risk zonation. The resulting risk zonation map classifies the study area into high-risk (3%), medium-risk (14%), and low-risk (83%) zones. This study primarily addresses the 3% high-risk area where landslides pose a significant threat to population and infrastructure, aiming to inform policy implementation and mitigation measures.

AbstractLandslide susceptibility mapping plays an imperative role in mitigating hazards and determining the future direction of developmental activities in mountainous regions. Here, we used 518 landslide occurrences and nine landslide-conditioning parameters to build landslide vulnerability models in the Kailash Sacred Landscape (KSL), India. Four multivariate statistical models were applied, namely the generalized linear model (GLM), maximum entropy (MaxEnt), Mahalanobis D2 (MD), and support vector machine (SVM), to calibrate and compare four maps of landslide susceptibility. The results demonstrated the outperformance of Mahalanobis D2 for predictability compared to other models obtained from the area under the receiver operating characteristic curve (ROC). The ensemble model data shows that 10.5% of the landscape has susceptible conditions for future landslides, whereas 89.50% of the landscape falls under the safe zone. The occurrence of landslides in the KSL is linked to the middle elevations, vicinity to water bodies, and the motorable roads. Furthermore, the observed patterns and the resulting models exhibit the major variables that cause landslides and their respective significance. The current modelling approach could provide baseline data at the regional scale to improve the developmental planning in the KSL.

Abstract Landslide vulnerability prediction maps are among the most important tools for managing natural hazards associated with slope stability in river basins that affect ecosystems, properties, infrastructure and society. Landslide events are among the most hazardous patterns of slope instability in the coastal mountains of Syria. Thus, the main goals of this research are to evaluate the performance of three different statistical outputs: Frequency Ratio (FR), Statistical Index (SI) and Index of Entropy (IoE) and therefore map landslide susceptibility in the coastal region of Syria. To this end, we identified a total of 446 locations of landslide events, based on the preliminary inventory map derived from fieldwork and high-resolution imagery surveys. In this regard, 13 geo-environmental factors that have a high influence on landslides were selected for landslide susceptibility mapping. The results indicated that the FR method outperformed the SI and IoE models with a high AUC of 0.824 and better adaptability, followed by the SI with 0.791. According to the SCAI values, although the FR model achieved the best reliability, the other two models also showed good capability in determining landslide susceptibility. The result of FR-based modelling showed that 18.51 and 19.98% of the study area fall under the high and very high landslide susceptible categories, respectively. In the map generated by the SI method, about 36% of the study area is classified as having high or very high landslide sensitivity. In the IoE method, whereas 14.18 and 25.62% of the study area were classified as “very high susceptible” and “high susceptible,” respectively. The relative importance analysis demonstrated that the slope aspects, lithology and proximity to roads effectively motivated the acceleration of slope material instability and were the most influential in both the FR and SI models. On the other hand, the IoE model indicated that the proximity to faults and roads, along with the lithology factor, were important influences in the formation of landslide events. As a result, the statistical bivariate models-based landslide mapping provided a reliable and systematic approach to guide the long-term strategic planning procedures in the study area.

Abstract In many countries like Pakistan, where crops are irrigated by wastewater, the accumulation of heavy metals is a serious problem, especially when such an irrigation is a widespread practice. The focus of this study was to know the highly toxic metals like cadmium (Cd), chromium (Cr), and lead (Pb) in water, agricultural soil, and crops, besides their probable risk to human health in the area of Vehari district. The physicochemical parameters were determined for the samples, including organic matter, organic carbon, pH, and electrical conductivity. Water used for irrigation, samples of vegetables for Cd, Cr, and Pb concentration, as well as transfer factor from soil to plants (TF) were analyzed for calculating the daily intake of metals (DIM) and their health risk index (HRI). The results show that the wastewater used for irrigation was contaminated with Cr (0.07mg/kg), Cd (0.054mg/kg), and Pb (0.38mg/kg). In the tube well, the concentrations of heavy metals were: Cd (0.053mg/kg), Pb (0.01mg/kg), and Cd (0.03mg/kg). Application of wastewater increased heavy metals concentration in soil and vegetables. Heavy metals concentrations in wastewater irrigated soil before sowing vegetables in mg/kg were: Pb (0.91), Cd (0.12), and Cr (0.48). After the application of wastewater, significant enrichment of wastewater was observed in Pb (1.93mg/kg), Cd (0.07mg/kg), and Cr (0.34mg/kg). Our study showed a high-risk index of food crops polluted with heavy metals and resultantly greater health risk to humans and animals. That is why preventive measures should be adopted to reduce heavy metals pollution to irrigation water and soils to protect both humans and animals in the Vehari district.

Abstract Landslides in mountainous areas are one of the most important natural hazards and potentially cause severe damage and loss of human life. In order to reduce this damage, it is essential to determine the potentially vulnerable sites. The objective of this study was to produce a landslide vulnerability map using the weight of evidence method (WoE), Radial Basis Function Network (RBFN), and Support Vector Machine (SVM) for the N'fis basin located on the northern border of the Marrakech High Atlas, a mountainous area prone to landslides. Firstly, an inventory of historical landslides was carried out based on the interpretation of satellite images and field surveys. A total of 156 historical landslide events were mapped in the study area. 70% of the data from this inventory (110 events) was used for model training and the remaining 30% (46 events) for model validation. Next, fourteen thematic maps of landslide causative factors, including lithology, slope, elevation, profile curvature, slope aspect, distance to rivers, topographic moisture index (TWI), topographic position index (TPI), distance to faults, distance to roads, normalized difference vegetation index (NDVI), precipitation, land use/land cover (LULC), and soil type, were determined and created using the available spatial database. Finally, landslide susceptibility maps of the N'fis basin were produced using the three models: WoE, RBFN, and SVM. The results were validated using several statistical indices and a receiver operating characteristic curve. The AUC values for the SVM, RBFN, and WoE models were 94.37%, 93.68%, and 83.72%, respectively. Hence, we can conclude that the SVM and RBFN models have better predictive capabilities than the WoE model. The obtained susceptibility maps could be helpful to the local decision-makers for LULC planning and risk mitigation.

A tropical cyclone is a significant natural phenomenon that results in substantial socio-economic and environmental damage. These catastrophes impact millions of people every year, with those who live close to coastal areas being particularly affected. With a few coastal cities with large population densities, Tamil Nadu’s coast is the third-most cyclone-prone state in India. This study involves the generation of a cyclone risk map by utilizing four distinct components: hazards, exposure, vulnerability, and mitigation. The study employed a Geographical Information System (GIS) and an Analytical Hierarchical Process (AHP) technique to compute an integrated risk index considering 16 spatial variables. The study was validated by the devastating cyclone GAJA in 2018. The resulting risk assessment shows the cyclone risk is higher in zones 1 and 2 in the study area and emphasizes the variations in mitigation impact on cyclone risk in zones 4 and 5. The risk maps demonstrate that low-lying areas near the coast, comprising about 3%, are perceived as having the adaptive capacity for disaster mitigation and are at heightened risk from cyclones regarding population and assets. The present study can offer valuable guidance for enhancing natural hazard preparedness and mitigation measures in the coastal region of Tamil Nadu.

This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 12.84% according to the WoE and IV models, respectively. Finally, the resultant maps were validated using the success and prediction rate curves, seed cell area index (SCAI), and R-index approaches. The success rate curve validated the results at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV was 83.34 and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined, and the risk map of the study area was generated. According to the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a very high-risk zone. It was found from the analysis that for assessing landslide susceptibility, both the models are suitable and applicable in the Hindu Kush region.

Neighborhood services, property attributes, and their associated amenities have positive impacts on land and property values. This impact is estimated by the hedonic pricing model, which is considered an effective method used in previous studies for such evaluations. The study uses Geographical Information Science by digitizing the point of interest in the study area for spatial modeling of data collection points and multi-linear regression as a statistical analysis of hedonic measurements. The hedonic measurements include the data of structural, locational, environmental, and community attributes of a property at a given time and space at a walkable distance from the neighborhood for measuring proximity. The results of the study are represented through the summary of the regression model, which expresses the impact of every individual variable on the entire value of the property, and the appropriateness of the results is shown by values R, R2, and adjusted R2. The result of the study concluded that property characteristics are varied from location to location, and that is why it is difficult to measure the exact market values, particularly in areas that lack urban planning and heterogeneous data. Research on such burning issues is essential for sustainable urban development.

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g., human losses, economic impact, and environmental degradation) that may occur in the future (e.g., sinkholes) and can be further utilized in perspective for a sinkhole early warning system.

Plant diversity is lower in arid regions around the world. However, semi-arid regions have very high species richness and are notable in terms of vegetation structure and plant diversity. The major goal of this study was to assess the composition and diversity of woody species with respect to edaphic properties in semi-arid lowlands of Kasur, Punjab Pakistan. Comprehensive field surveys were conducted to study the botanical diversity of woody vegetation in Kasur district (Punjab) during 2020–2022. Based on geography and vegetation richness, 120 sampling sites were selected, with each site comprising 05 transects of 500 m2 randomly. Therefore, from each sampling site, 25 quadrats were taken for exploring the biological assortment of woody vegetation. Soil samples were collected at a depth of 9 to 12 cm and placed in a polythene bag. The soil samples were tested for soil pH, EC, OM, macronutrients (N, P, and K), and cation exchange capacity (CEC). A total of 86 woody species belonging to 61 genera and 26 families were documented from diverse habitats of semi-arid lowland of Kasur, Punjab, Pakistan. The leading family was Leguminosae having 13 genera and 21 species, followed by Moraceae, including four genera and nine species, and Bignoniaceae with seven genera and 09 species. Overall, five woody plant communities were renamed by means of the maximum indictor-valued plant species, such as those listed below; 1. EDM: Eucalyptus-Dalbergia-Morus community; 2. PBM: Populus-Bombax-Morus community; 3. ZTZ: Ziziphus-Tamarix- Ziziphus community; 4. PAP: Prosopis-Acacia-Prosopis community, and 5. BCR: Broussonetia-Conocarpus-Ricinus community. The CCA ordination makes it apparent that appropriate edaphic aspects, such as pH, organic matter, N, P, K, soil texture (silt, sand, and clay), and cation exchange capacity (CEC), have a significant influence on how woody species are distributed. The current effort provides a fundamental layout to realize the influences of environmental variables on the arrangement, variety, and relations of woody vegetation, which is useful to improve the conservation and management events for the ecological restoration of degraded habitation in the studied district.

The present study examines indoor and outdoor environmental particulate matter and gaseous pollutants in order to evaluate the urban air quality, the sources and pathways of pollutants, and its impact on Chennai megacity, South India. A total number of 25 air conditioner filter particulate matter samples collected from residential buildings, schools, colleges, commercial shopping malls, and buildings near urban highways were studied for indoor air quality. Similarly, outdoor air quality assessments have been done in various parts of the Chennai metropolis, including the Manali-Industrial area, the Velachery-Residential site, and the Alandur Bus Depot, as well as collected air quality data sets from the Central Pollution Control Board at continuous ambient air quality monitoring stations. The suspended atmospheric particles where the highest concentration (47%) occurred were mostly located in the roadside environments followed by commercial areas (42%), which indicates the increase in air pollution in the roadside areas. Further, environmental magnetism and ecological risk indices were studied from the collected data set. The study predicts that the air pollutants were predominantly from anthropogenic sources, such as vehicle emissions, effluents from power plants, abrasion of tires, steelworks, burning of fossil fuels and construction materials, etc. As a result, the current study suggests 68% of indoor pollutants were from the anthropogenic input, 18% from the pedogenic origin, and 14% from high heavy metal pollution at the sampling sites. This indicates that raising the ventilation rate via mechanical components significantly enhances the indoor air quality. These findings might be valuable in improving urban air quality, reducing traffic-related pollutants, and improving environmental quality.

Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society.