In the present study, winter fog events (Nov. 2017-Feb. 2018 in Amritsar city were compared with other major cities of North India. Multivariate data analysis, along with air mass trajectory analysis, was used to explain the complex behaviour of ambient air quality during winter fog. Average particulate matter (PM) during fog events was PM 2.5 (77 μg m −3 ), PM 10 (162 μg m −3 ) above the 24 h average National Ambient Air Quality Standards (NAAQS) of PM 2.5 (60 μg m −3 ) and PM 10 (100 μg m −3 ), respectively prescribed by Government of India. Wind speed and visibility during fog events were studied along with prevailing wind direction for major PM episodes. Amritsar's PM 2.5 comparison with Ludhiana, Delhi, Kanpur, Lucknow, and Jaipur showed a clear link between Amritsar with Ludhiana (r = 0.807), a North Indian industrial hub. Lucknow and Kanpur had a strong correlation (r = 0.826) due to their proximity. Box-plot of PM 2.5 to PM 10 ratio revealed a lower contribution of PM 2.5 in Amritsar as compared to other cities. Dimensionality reduction using factor analysis of ambient air quality and meteorological parameters grouped the data in order of their variance explained. The first principal component (PC-1) was PM 2.5 and PM 10 , followed by an antagonist correlation of humidity with wind velocity and visibility in Amritsar city. Factor analysis of ambient air quality of six cities, grouped Delhi, Kanpur, and Lucknow into PC-1, followed by Ludhiana and Amritsar as PC-2 which could be due to their proximity signifying the similar ambient air quality of the sites. In order to determine the origin of air mass, 24 h backward trajectories were studied and corroborated with wind rose profile. The results revealed the transport of air masses from the west to the source location.

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Tungsten Inert Gas (TIG) welding is one of the widely used techniques for joining ferrous and non-ferrous metals. The TIG welding parameters are the most significant factors affecting the quality, productivity and cost of welding. In this study, SS 202 and SS 316 types of austenitic stainless steels were welded by GTAW (Gas Tungsten Arc Welding) using SS 202 and SS 316 (ER 316L) filler metals, respectively. The present study aims to search out the optimization of process parameters for Gas Tungsten Arc Welding (GTAW). The process parameters like welding current, wire diameter, shielding gas, and groove angle were varied at different levels to find out the influence of parameters on Hardness and Toughness. After performing Charpy Impact Test on SS 202 steel at room temperature, the results show that toughness mainly depends on the shielding gas chosen whereas for SS 316 steel the toughness increases as well as decreases as per the changes in the process parameters. Up to the magnitude of 150 A, the Microhardness value increases and then decreases with further rise in its magnitude. Keywords: GTAW, SS 202, SS 316, Microhardness, Toughness I.INTRODUCTION Tungsten Inert Gas (TIG) welding which uses a non-consumable tungsten electrode and an inert gas for arc shielding, is a highly relevant arc welding process. It is frequently used for welding hard-to-weld metals such as aluminium, stainless steel, magnesium, and titanium. TIG welding is a multi-objective and multi-factor metal fabrication technique, where Argon or Helium is used for shielding purpose. TIG weld quality is strongly characterized by the weld pool geometry because it plays a major role in determining the mechanical properties of the weld. Therefore, it becomes necessary to select the welding process parameters for obtaining an optimal weld pool geometry. Optimization of weld pool geometry has been attained by using TIG welding on a stainless-steel plate by varying welding parameters, and results showed that 'higher the better' quality characteristics are better used in the analysis of Signal-to-Noise ratio (S/N) and analysis of variance (ANOVA). A mathematical model has been developed to study the effects of process parameters on weld pool geometry in GTAW using ANOVA and found that wire feed rate, travel speed, and wire diameter are the major parameters that influence bead geometry in GTAW. Investigation of Weldments obtained in stainless steel (304L and 316L) by GTAW yields better mechanical properties than by GMAW, i.e., yield strength, tensile strength, hardness and impact energy values of 304L and 316L stainless steels welded by GTAW are higher than that welded by GMAW. It optimized SS 316L stainless steel and observed that Gas flow has a major impact and Bevel angle has the slightest impact in affecting the tensile strength. Use of active flux, TiO2 in GTAW increases the depth of penetration. Peak current has the highest contribution in affecting the micro weld hardness; grain size and HAZ width in the GTA Welded Aluminium Alloy 7039. N...

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Winter fog is a complex issue affecting human health and is responsible for higher numbers of traffic accidents in North India, which is further aggravated due to atmospheric pollutants. An indigenous glass-plate fog collector was used to collect fog water from December 2020 to February 2021. Thirty samples of fog water were collected from the rooftop of an academic building at Guru Nanak Dev University, Amritsar, in order to study the chemistry of fog water. The studied parameters were pH, electrical conductivity (EC), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), potassium (K+) sulphate (SO42−), nitrate (NO3−), chloride (Cl−), aluminum (Al), lead (Pb) and zinc (Zn). The average values were as follows: pH (4.6–7.5), EC (135 µS/cm), SO42− (77.5 ppm), Cl− (9.9 ppm), NO3− (9.3 ppm), Ca2+ (8.1 ppm), Mg2+ (2.0 ppm), K+ (2.0 ppm), Na+ (1.6 ppm), Zn (218 ppb), Al (60.8 ppb) and Pb (8.8 ppb). Cation–anion balance was used to assess the data’s reliability. The enrichment factor (EF) was utilized to distinguish between crustal and anthropogenic sources. SO42−, NO3−, Cl− and K+ originated from anthropogenic sources, whereas Mg2+ and Na+ came from crustal sources. The molar ratio of sulphate to nitrate was 10.6, which indicates a greater contribution from the combustion of fossil fuels and stack emissions. Ionic species were subjected to principal component analysis (PCA) as a dimensionality reduction approach and to group species with comparable behavior. Three principal components (PC) that together accounted for 77.5 percent of the total variance were identified by PCA. Backward trajectory analysis of air masses was performed to determine their origin, and two major clusters explained 89 percent of the contribution of air masses, primarily from the north-east and north directions. To gain a comprehensive understanding of fog water, a global perspective on pH, EC and ionic species is considered.