Forest fires are among the most major causes of global ecosystem degradation. The integration of spatial information from various sources using statistical analyses in the GIS environment is an original tool in managing the spread of forest fires, which is one of the most significant natural hazards in the western region of Syria. Moreover, the western region of Syria is characterized by a significant lack of data to assess forest fire susceptibility as one of the most significant consequences of the current war. This study aimed to conduct a performance comparison of frequency ratio (FR) and analytic hierarchy process (AHP) techniques in delineating the spatial distribution of forest fire susceptibility in the Al-Draikich region, located in the western region of Syria. An inventory map of historical forest fire events was produced by spatially digitizing 32 fire incidents during the summers of 2019, 2020, and 2021. The forest fire events were divided into a training dataset with 70% (22 events) and a test dataset with 30% (10 events). Subsequently, FR and AHP techniques were used to associate the training data set with the 13 driving factors: slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Topographic Wetness Index (TWI), rainfall, temperature, wind speed, TWI, and distance to settlements, rivers and roads. The accuracy of the maps resulting from the modeling process was checked using the validation dataset and receiver operating characteristics (ROC) curves with the area under the curve (AUC). The FR method with AUC = 0.864 achieved the highest value compared to the AHP method with AUC = 0.838. The outcomes of this assessment provide constructive spatial insights for adopting forest management strategies in the study area, especially in light of the consequences of the current war.

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (<5.0) and Boruta feature ranking (<10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps.

Renewable energy sources are the most necessitated natural energy to reduce fossil fuels globally. Fossil fuel is the most valuable and limited resource on the planet, but on the other hand, renewable energy creates less pollution. Solar energy is the most effective renewable resource for daily use. Solar power plants are necessary for domestic and daily use. Remote sensing and geographic information technology (GIS) were used for this study to delineate the possible site selection of solar power plants in Kolkata and the surrounding area in West Bengal, India. The analytical hierarchy process (AHP) and the multi-criteria decision-making process (MCDA) were used for each weight calculation and ArcGIS v10.8 was applied for weighted overlay analysis (WOA) for delineation of the result. The site suitability map was developed using a pairwise comparison matrix and the weights were calculated for each criterion. The suitability map was divided into five categories, from not suitable to very highly suitable. A total of 474.21 km² (10.69%) of the area was classified as very highly suitable whereas 249.54 km² (5.62%) area was classified as not suitable because of the water area and east Kolkata wetland. A total of 1438.15 km² (32.43%) of the area was classified as highly suitable for a solar power plant. The Kolkata megacity and water body locations were identified as moderate to not suitable sites. Very high and high-potential sites were identified 2 to 5 km from the central business district (CBD) location, which is Dharmotala. Renewable energy source is needed in the megacity of Kolkata. If solar power plants are contracted then the demand for fossil fuel will be reduced one day, and that will help the environment as well as the society in terms of sustainable development. This study result is helpful for administrators, urban planners, developers, and other stakeholders for the implementation and development of a new solar power plant in the study area.

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.

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.