Water vapour is highly variable over tropical region and sensitive to weather condition, monsoon onset, green house effect, and pollution level in Ganga River. In the present study, variability in water vapour derived from Global Positioning System (GPS) over Varanasi (25$^{\circ}$20$^{\prime}$N, 82$^{\circ}$59$^{\prime}$E) during the period 2007–2010 has been studied. The GPS-derived water vapour (WV) has been compared with those retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) and ECMWF. The GPS-WV data concurrent to MODIS and ECMWF timing has been correlated to perform further analysis. To study the accuracy of water vapour retrieved from the MODIS and ECMWF, root mean square error (RMSE), absolute error (AE), correlation and standard deviation in it are computed with respect to GPS-derived water vapour. Analysis shows an annual correlation $R^{2}$ = 86%, RMSE = 9.5 mm and AE (MODIS–GPS) = 7.0 mm in MODIS retrieval and annual correlation R$^{2}$ = 86%, RMSE = 6.1 mm and AE (ECMWF–GPS) = 2.4 mm in ECMWF reanalysis retrieval. Correlation of ECMWF and MODIS datasets with the GPS datasets are found to vary significantly with seasons. The correlation is high during monsoon season and low during spring season. Water vapour is found to be an indicator for the onset of monsoon.

$\bf{Highlights}$

$\bullet$ Accuracy of water vapor (WV) retrieved from the MODIS and ECMWF with respect to GPS WV.

$\bullet$ High Annual correlation of $R^{2}$ = 0.86 between both MODIS–GPS and ECMWF–GPS.

$\bullet$ The correlation is high during monsoon season and low during spring season.

$\bullet$ The performance of ECMWF is found to be better than that of MODIS.

Fluoride (F$^{-}$ ) pollution in groundwater of the Older Alluvial Plain (OAP) of Delhi has been reported as a major problem. About 34% of the groundwater samples collected for this study had F$^{-}$ level beyond the permissible limit; with F$^{-}$ concentration in the range of 0.14–3.15 mg/L (average 1.20 mg/L). In this context, this article for the first time attempts on the genesis of major ions chemistry and F$^{-}$ pollution in groundwater of OAP Delhi by going beyond the statistical analysis to sediment geochemistry, chemical weathering processes and understanding of the processes using stable environmental isotopes ($^{2}$H and $^{18}$O). The XRD of the OAP sediments revealed the dominance of fluor-biotite, albite, calcite, quartz, and chlorite. Whereas, the separated clay revealed the dominance of chlorite, kaolinite, and illite minerals. The saturation index (SI) values indicated that the groundwater chemistry is in the process of further F$^{-}$ enrichment by way of sediment groundwater interaction. With the given mineralogy of the sediments, the dominance of major ions like Na$^{+}$), K$^{+}$, Mg$^{2+}$, Ca$^{2+}$, Cl$^{-}$ and F$^{-}$ has been attributed to chemical weathering of biotites, phlogopites, albite, and calcite during sediment–water interaction. While the dominance of SO$_{4}$ $^{2-}$ has been attributed to anthropogenic sources and confirmed by its association with heavier stable isotopes of hydrogen ($\delta^{2}$H: −50.44 to −40.02 per thousand) and oxygen ($\delta^{18}$O: −7.19 to −5.62 per thousand) indicating evaporative enrichment during isotopic fractionation.

Air pollution is one of the biggest problems worldwide and needs to be addressed potentially with the implementation of updated stringent policies and legislative laws. The nationwide lockdown imposed to prevent the COVID-19 outbreak, has given us a unique opportunity to understand the contribution of anthropogenic emissions to the total atmospheric pollutant burden on a global as well as regional scale. Thus, in the present study, we try to investigate the impact of COVID-19 induced lockdown on common ambient air pollutants (i.e., PM$_{2.5}$, NO$_2$, and SO$_2$) concentration over 22 cities in India using in-situ measurement under a network of Centre Pollution and Control Board (CPCB). A significant reduction in the mean mass concentration of all the studied air pollutants (i.e., PM$_{2.5}$, NO$_2$, and SO$_2$) (nearly 10–70%) is found during different phases of lockdown which reached within the National Ambient Air Quality Standard (i.e., NAAQS). The reduction in studied air pollutants is more prominent during the first phase of lockdown (mainly NO$_2$) which could be due to the complete shutdown of industrial activities. The outcome of the present study will be helpful for policymakers to design cost-effective and accurate air pollution mitigation strategies for the development of a sustainable environment. The study also suggests that well-planned short-term and periodical lockdown could be an alternative effective tool of air pollution mitigation.

An investigation is conducted to study the uncertainty in the cyclonic ocean wind speed (CWS) of Cyclone Global Navigation Satellite System (CYGNSS) satellites over the North Indian Ocean (NIO). For the completion of the assessment, the comparison of CYGNSS wind speed during normal ocean conditions is also carried out with respect to buoy and advanced scatterometer (ASCAT ) data. The decollocation effect is also studied using the India Meteorological Department’s (IMD) data. The sensitivity of the uncertainty in CWS is evaluated through the soil moisture active passive (SMAP) data. It has been found that the CYGNSS CWS underestimates for CWS ${\ge}$35.0 m/s. The study shows that the uncertainty for the CWS ${\ge}$35.0 m/s is dependent on the incidence angle. Therefore, a new incidence angle-based Geophysical Model Function (IGMF) is proposed for CWS. The IMD and SMAP data for the cyclones of the year 2018–2019 have been used for validation of the CWS derived from IGMF. An extremely severe cyclone Fani is analysed in detail. The IGMF reduces the mean error from 1.77 to –0.001 m/s with respect to SMAP for Fani. Overall, the improvement in root mean square difference (RMSD) and the mean error with IGMF are 16.11% and 44.39%, respectively, over CYGNSS for CWS ${\ge}$35.0 m/s. The maximum CWS from CYGNSS, IGMF, and IMD is 38.8, 52.07, and 54.97 m/s, respectively, for the Bay of Bengal. It can be concluded that IGMF provides better CWS than the CYGNSS CWS.

$\bf{Highlights}$

$\bullet$ The uncertainty of the CYGNSS Cyclonic Ocean Wind Speed (CWS) is investigated over the North Indian Ocean for the years 2018-2019.

$\bullet$ The Geo-shifting process with the collocation is included to reduce the collocation error due to the cyclone translational speed.

$\bullet$ The CYGNSS CWS shows significant under-estimation for the wind speed >35 m/s with the SMAP and the IMD data.

$\bullet$ A new Incidence angle-based Geo-physical Model Function (IGMF) is developed to improve the bias in the CWS.

$\bullet$ The IGMF model has better agreement with both SMAP and IMD data than CYGNSS CWS over the NIO region.