The natural fiber-reinforced polymer composite materials offered extensive range of properties which are suitable for large number of engineering application. The natural fibers have been abundantly available in the world. It has unique properties compared to synthetic fiber and reduces the plastic usage. This article reports the extraction process of natural fibers, characterization of natural fibers, and preparation of natural fiber-reinforced composites. The mechanical properties such as tensile, flexural, impact, and dynamic properties as well as thermal and machinability properties of the composites with and without chemically treated fibers were reported. The water absorption capability of the composites and its effect on mechanical properties were also reported.

Natural cellulose fibers were newly identified from the sources of sansevieria ehrenbergii plant. These fibers were extracted using the mechanical decortication process. The hierarchical cell structure of the plant and fibers was analyzed using scanning electron microscope, optical microscope, Fourier transforms infrared, and X-ray diffraction. The density and diameter of the fibers were found to be approximately 0.887 g/cm³ and 10–250 μm, respectively. The various chemical compositions were analyzed and compared with other natural fibers. The thermal stability of the fiber was examined through thermogravimetric analysis/differential thermogravimetric analysis (DTG). The maximum peak temperature was obtained at 333.02 °C in DTG curve. The raw fibers exhibited a tensile strength of 50–585 MPa, an elongation at break of 2.8–21.7%, a Young’s modulus of 2.5–7.5 GPa, and a corrected compliances Young’s modulus of 2.5–7.8 GPa.

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM_2.5, PM₁₀, O₃, CO, SO₂, NO₂, CO₂) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R², RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.