A laccase producing new bacterial strain (Alcaligenes faecalis XF1) was isolated from green site of Chandigarh (India) by standard screening method. Nutrient broth medium containing 0.2 mM CuSO4 was used for the production of laccase. Maximum production (110 U/ml) was achieved after four days of incubation. The extracellular laccase from the medium was purified by simple salt precipitation and ion exchange technique to get 3.8 fold purified protein with 1637.8 U/mg specific activity. Purified laccase (named as LAC1*) revealed its optimum activity at pH 8.0 and 80 °C temperature, and displayed remarkable stability in the range of 70-90 °C and in the pH range (pH 7.0-9.0). The single bands on SDS-PAGE represents the purity of LAC1* with molecular weight of ∼71 kDa. The kinetic parameters for 2,6-DMP oxidation was Km, Vmax and kcat were 480 μM, 110 U/mL and 1375 s-1. Enzyme activity of the LAC1* was significantly enhanced by Cu2+, Mg2+, Mn2+, SDS and NaCl, and was slightly inhibited in the presence of conventional inhibitors like cysteine, EDTA and sodium azide. Extracellular nature and significant stability of LAC1* under extreme conditions of temperature, pH, heavy metals, halides and detergents confined its suitability for various biotechnological and industrial applications which required these qualities of laccase. So after recognizing all these properties the purified laccase was studied for its application in decolorization of industrial dyes.

An assessment and prediction of PM_2.5 for a port city of eastern peninsular India is presented. Fifteen machine learning (ML) regression models were trained, tested and implemented to predict the PM_2.5 concentration. The predicting ability of regression models was validated using air pollutants and meteorological parameters as input variables collected from sites located at Visakhapatnam, a port city on the eastern side of peninsular India, for the assessment period 2018–2019. Highly correlated air pollutants and meteorological parameters with PM_2.5 concentration were evaluated and presented during the period under study. It was found that the CatBoost regression model outperformed all other employed regression models in predicting PM_2.5 concentration with an R² score (coefficient of determination) of 0.81, median absolute error (MedAE) of 6.95 µg/m³, mean absolute percentage error (MAPE) of 0.29, root mean square error (RMSE) of 11.42 µg/m³ and mean absolute error (MAE) of 9.07 µg/m³. High PM_2.5 concentration prediction results in contrast to Indian standards were also presented. In depth seasonal assessments of PM_2.5 concentration were presented, to show variance in PM_2.5 concentration during dominant seasons.