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Purpose The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM2.5 through wavelet and regression analysis. Design/methodology/approach In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM2.5 as a dependent variable. Findings It is found that few pollutants such as NO2, NOx, SO2, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM2.5. The regression model developed has high R2 value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model. Research limitations/implications The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM2.5. Practical implications Control measures such as control charts can be implemented for significant factors. Social implications Rules and regulations can be made more stringent on the factors. Originality/value The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

In the present investigation, the mechanical and wear properties of aerospace alloy AA2124-9 wt% of B4C composites were displayed. The composites containing 9 wt% of micro boron carbide in AA2124 alloy were synthesized by liquid metallurgy method through stir casting. For the composites, reinforcement particles were preheated to a temperature of 400℃ and afterward added in ventures of two stages into the vortex of liquid AA2124 alloy compound to improve the wettability and dispersion. Microstructural examination was carried out by SEM and elemental investigation was finished by EDS. Mechanical and wear properties of as cast AA2124 alloy and AA214-9 wt% of B4C composites were evaluated as per ASTM standards. Microstructural characterization by SEM and EDS confirmed the distribution and presence of micro boron carbide particles in the AA2124 alloy matrix. The hardness, ultimate strength, yield strength and bending behaviour of AA2124 alloy enhanced with the incorporation of 9 wt% of micro B4C particles. The hardness of as-cast AA2124 alloy was 65.76 BHN; it is 96.7 BHN in 9 wt% of B4C reinforced composites. The ultimate and yield strength of AA2124 alloy was 187.08 MPa and 150.33 MPa respectively. The enhanced UTS and YS in 9 wt% of B4C reinforced composites were 254.1 MPa and 203.7 MPa, respectively.  Further, ductility of AA2124 alloy decreased with the presence of B4C particles. Wear resistance of aerospace alloy increased with the addition of micro particles. Tensile fractography and worn surface morphology were studied on the tested samples to know the various fractured and wear mechanisms.

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