Abstract:
Rainwater quality studies were conducted during July to September of 2016–2019 at a sub-tropical urban location in north India, viz. Roorkee (29.8543° N, 77.8880° E). The parameters pH, electrical conductivity, and major chemical constituents were measured for the precipitation samples. The volume-weighted mean electrical conductivity values were in the range 21.57 ± 1.63, and the pH values indicated the precipitation events to be mostly alkaline. The ionic concentration in the study area followed the order of: \(\:{Ca}^{2+}>HC{O}_{3}^{-}>C{l}^{-}>{Na}^{+}>N{O}_{3}^{-}>S{O}_{4}^{2-}>{Mg}^{2+}>{K}^{+}\). The dominant ions observed alongwith their weighted mean concentration values were as Ca 2+ (115.19 ± 96 µeq/l), HCO 3 − (90.95 ± 64 µeq/l), Cl − (58.30 ± 38 µeq/l), Na + (38.42 ± 22 µeq/l), and NO 3 − (36.22 ± 23 µeq/l). Aggregates of the acidic species \(\:\left(S{O}_{4}^{2-}+{NO}_{3}^{-}\right)\) and the major cations \(\:\left({Ca}^{2+}+{Mg}^{2+}\right)\) respectively showed a substantial correlation (r = 0.83; P < 0.001), highlighting that the presence of alkaline species in the rainwater could act as acidity neutralizing agents. The presence of sulphates in water was assessed to the order of 85%, whereas nitrate presence was around 15%. Both of these were apparently contributed through anthropogenic activities, driven by combustion of fossil fuel or biomass etc. Principal Component Analysis (PCA) was employed to transform the original variables into a new set of uncorrelated principal components, and to effectively reduce the dimensionality of the dataset while still capturing the majority of the variability in Rainwater quality. The resulting principal components (P1 to P4) pointed towards key factors influencing water quality. For assessing the temporal variability in the rainwater quality during different phases of the monsoon season over a period of few years, the ANOVA-derived hypotheses were applied. Backward trajectory analysis was applied as a tool for investigating the transport and origin of air pollutants. Using a combination of meteorological data and trajectory analysis tools, the study evaluated the potential of each site for capturing regional and long-range transport of pollutants.