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.

The present research is the first type of study in which the application of powder mixed electrical discharge machining (PMEDM) for the machining of β-phase titanium (β-Ti) alloy has been proposed. β-Ti alloys are new range of titanium alloys, which has a wide-spread application in dental, orthopedics, shape memory, and stents. The aim of the present study is to fabricate submicro- and nanoscale topography by PMEDM process to enhance the biocompatibility without affecting machining efficiency. The effect of Si powder concentration along with pulse current and duration on the surface and machining characteristics has been investigated. A significant decrease in surface crack density on the machined surface with 4 g/l Si powder concentration was observed. When β-Ti alloy was modified at 15 A pulse current, longer pulse interval with 8 g/l concentration of Si powder particles, the interconnected surface porosities with pore size 200–500 nm was observed. Moreover, at Si powder concentrations of 2 g/l and 4 g/l, the recast layer thickness is 8 µm and 2–3 µm, respectively. Elemental mapping analysis confirmed that PMEDM also generated carbides and oxides enriched surface, a favorable surface chemistry to enhance the biocompatibility of β-Ti alloy. Furthermore, PMEDM also enhances the machining performance by improving material removal rate and reducing tool wear rate.