Land use land cover (LULC) has altered dramatically because of anthropogenic activities, particularly in places where climate change and population growth are severe. The geographic information system (GIS) and remote sensing are widely used techniques for monitoring LULC changes. This study aimed to assess the LULC changes and predict future trends in Selangor, Malaysia. The satellite images from 1991–2021 were classified to develop LULC maps using support vector machine (SVM) classification in ArcGIS. The image classification was based on six different LULC classes, i.e., (i) water, (ii) developed, (iii) barren, (iv) forest, (v) agriculture, and (vi) wetlands. The resulting LULC maps illustrated the area changes from 1991 to 2021 in different classes, where developed, barren, and water lands increased by 15.54%, 1.95%, and 0.53%, respectively. However, agricultural, forest, and wetlands decreased by 3.07%, 14.01%, and 0.94%, respectively. The cellular automata-artificial neural network (CA-ANN) technique was used to predict the LULC changes from 2031–2051. The percentage of correctness for the simulation was 82.43%, and overall kappa value was 0.72. The prediction maps from 2031–2051 illustrated decreasing trends in (i) agricultural by 3.73%, (ii) forest by 1.09%, (iii) barren by 0.21%, (iv) wetlands by 0.06%, and (v) water by 0.04% and increasing trends in (vi) developed by 5.12%. The outcomes of this study provide crucial knowledge that may help in developing future sustainable planning and management, as well as assist authorities in making informed decisions to improve environmental and ecological conditions.

The rapid increase in global energy demand and the depletion of fossil fuels highlight the importance of biomass as a renewable energy source. Biomass, especially agricultural by-products rich in hemicellulose, cellulose, and lignin, offers a sustainable alternative for producing fuels and value-added products. The generation of power, heat, and biofuels from biomass has gained increasing significance in today’s energy landscape. This review explores the potential of advanced biomass conversion technologies, with a focus on thermochemical and biochemical processes as key methods for optimizing agricultural waste management strategies. It examines these technologies for converting agricultural wastes into bioenergy, such as biogas, syngas, bio-oil, biochar, and digestate, while detailing their operational parameters, technological principles, and implementation prospects. While biological processes are limited by long processing times and low yields, thermochemical methods are challenged by high processing costs and temperature requirements. The review also compares the advantages and challenges of each method to assess their overall effectiveness and suitability. Further research in feedstock pretreatment, catalyst development, and system optimization is essential for advancing biofuel production.

The study examined the adsorption of hexavalent chromium [Cr(VI)] from aqueous solution by acidically prepared rice husk carbon (APRHC). APRHC was characterized in terms of surface area, micropore area, micropore volume, average pore diameter and surface morphology. The effects of pH, contact time, initial Cr(VI) concentration and adsorbent dose on the adsorption of Cr(VI) from aqueous solution were investigated. Batch adsorption tests showed that Cr(VI) adsorption depends on initial concentration, contact time and pH. Equilibrium adsorption was achieved in 120 min, while maximum Cr(VI) adsorption occurred at pH 2. An artificial neural network (ANN) was used to model Cr(VI) adsorption. The Levenberg–Marquardt (LM) training algorithm was found to be the best among the 11 backpropagation (BP) algorithms tested, with a lowest mean square error (MSE) of 8.8876 and highest coefficient of determination (R²) of 0.987. Adsorption of Cr(VI) by APRHC followed pseudo-second order kinetics. Langmuir and Freundlich isotherm equations were fitted to the equilibrium adsorption data; the former isotherm yielded a better fit. The thermodynamic results indicate that the process of Cr(VI) adsorption by APRHC was endothermic in nature. Desorption of Cr(VI) was very low, i.e. in the range from 0.1 to 9%. Cr(VI) adsorption capacity by APRHC was compared with that of various adsorbents. APRHC showed a high capacity for adsorption of Cr(VI). APRHC can be employed as an effective adsorbent and substitute for commercially available activated carbon for the removal of Cr(VI) from aqueous solutions and wastewater systems.

Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries in Southeast Asia. It is essential for sustainable development, particularly efficient water management practices, to know about the impact of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing were used for monitoring land use changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic information systems (QGIS) was performed for prediction of LULC changes. This study investigated the changes in LULC in the Perak River basin for the years 2000, 2010, and 2020. The study also provides predictions of future changes for the years 2030, 2040, and 2050. Landsat satellite images were utilized to monitor the land use changes. For the classification of Landsat images, maximum-likelihood supervised classification was implemented. The broad classification defines four main classes in the study area, including (i) waterbodies, (ii) agricultural lands, (iii) barren and urban lands, and (iv) dense forests. The outcomes revealed a considerable reduction in dense forests from the year 2000 to 2020, whereas a substantial increase in barren lands (up to 547.39 km²) had occurred by the year 2020, while urban land use has seen a rapid rise. The kappa coefficient was used to assess the validity of classified images, with an overall kappa coefficient of 0.86, 0.88, and 0.91 for the years 2000, 2010, and 2020, respectively. In addition, ANN-CA simulation results predicted that barren and urban lands will expand in the future at the expense of other classes in the years 2030, 2040, and 2050. However, a considerable decrease will occur in the area of dense forests in the simulated years. The study successfully presents LULC changes and future predictions highlighting significant pattern of land use change in the Perak River basin. This information could be helpful for land use administration and future planning in the region.