Vaibhav Shrivastava

To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India’s economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM_2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere’s natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World.

Forests are an important natural resource and are instrumental in sustaining environmental sustainability. Burning biomass in forests results in greenhouse gas emissions, many of which are long-lived. Precise and consistent broad-scale monitoring of fire intensity is a valuable tool for analyzing climate and ecological changes related to fire. Remote sensing and geographic information systems provide an opportunity to improve current practice’s accuracy and performance. Spectral indices techniques such as normalized burn ratio (NBR) have been used to identify burned areas utilizing satellite data, which aid in distinguishing burnt areas using their standard spectral responses. For this research, we created a split-panel web-based Google Earth Engine app for the geo-visualization of the region severely affected by forest fire using Sentinel 2 weekly composites. Then, we classified the burn severity in areas affected by forest fires in Wolgan Valley, New South Wales, Australia, and the surrounding area through Difference Normalized Burn Ratio (dNBR). The result revealed that the region’s burnt area increased to 6731 sq. km in December. We also assessed the impact of long-term rainfall and land surface temperature (LST) trends over the study region to justify such incidents. We further estimated the effect of such incidents on air quality by analyzing the changes in the column number density of carbon monoxide and nitrogen oxides. The result showed a significant increase of about 272% for Carbon monoxide and 45% for nitrogen oxides. We conclude that, despite fieldwork constraints, the usage of different NBR and web-based application platforms may be highly useful for forest management to consider the propagation of fire regimes.

Era-Interim (ECMWF Re- analysis) is a global reanalysis atmospheric product which is being continuously updated in real time since 1979. It is also termed as third generation reanalysis product. The Era–Interim gives meteorological products like precipitation, temperature, etc. In the present work, 3-hourly Era–Interim product for the entire India is compared with gridded data provided by IMD for period 1979–2013 and APHRODITE data for period 1979–2007, respectively. The comparison is done on seasonal basis and the seasons are taken based on the pattern of rainfall, hence, the four seasons selected are DJF (December, January & February), MAM (March, April & May), JJAS (June, July, August & September) and ON (October & November). In the methodology the Era-Interim 3-hourly products are converted into the daily products and then it is used to form seasonal images for each year. All the images are then taken to form four images as outcome for the entire study period which represents the average rainfall (mm/day) for the entire region. This is being done for the IMD and for the APHRODITE Data. All the four images are then taken for the comparison with the reference images of the IMD 0.5º × 0.5º gridded rainfall data and with the APHRODITE 0.5º × 0.5º gridded rainfall data. The correlation coefficient and the RMSE for each season is calculated. The mean value is compared with the mean of IMD and APHRODITE rainfall products, respectively and a bias in mean is also calculated along with the scatter plots of Era-Interim with the reference datasets. The Era – Interim data came out with suitable comparative parameters with high correlation coefficient and low RMSE value in certain regions and in specific seasons. Scatter plots have also given good correlation in all the seasons. Bias maps have also shown very less bias in specific seasons for certain regions. The suitability maps prepared for the study region also shows that most of the region lies in most suitable range and very less in unsuitable range.

Capacity studies of reservoirs are important to evaluate sedimentation and optimize reservoir operation schedule based on realistic assessment of available storage. Capacity study at regular interval provide information about rate and pattern of sedimentation between various levels, loss of capacity due to sedimentation, remaining time span of reservoir, etc. In the present study, evaluation of revised capacity of Gobind Sagar reservoir in Bilaspur district, Himachal Pradesh, India has been done using Google Earth Engine. Landsat 8 (OLI) data for September 2015 and for the period September 2017 to May 2018 covering full extent of Gobind Sagar reservoir is taken to compute the water spread area of this reservoir at different dates. Subsequently, the reservoir water level and volume of water stored on the corresponding dates is acquired from India-WRIS. By using trapezoidal formula capacity between two elevations is determined using water spread area obtained from Google Earth Engine analysis and elevation data obtained from India-WRIS. The comparison of water spread areas of different water levels, as obtained from remotely sensed data from September 2017 to May, 2018 with those from survey carried out during 1996/97, indicates a reduction in the capacity by 10.71% and sedimentation rate was estimated to be 14.24 Mm³/year.

The design and analysis of potable water distribution pipe network is one of the major task while planning any new city. Even finding the effectiveness and detecting any anomaly in the existing pipe line network is a very important to cut down losses and make a network all time efficient. This study makes an attempt to evaluate the existing water distribution network of Indian Institute of Remote Sensing (IIRS) and new pipe network to be developed under the master plan 2022. The new development induces an extra demand of 67634 litres per day (lpd). The Digital Globe image from Google Earth Pro is used for creating base layer and campus layout. ALOS PALSAR digital elevation model (DEM) is used to get elevation of nodes and tanks, which helps in alignment of pipes. Field survey was done for finding all the data necessary to make the database for input for EPANET 2.0. EPANET 2.0 pipe hydraulic model was used to test and design the existing and proposed potable water distribution pipe network. The results of evaluation of current operations show that the system can be made capable to fulfil the demand by increasing pumping time. But the future network operation requires an increase in pumping capability of 71 litre per minute (lpm) for filling Overhead tank. This study proves that geospatial technology is an efficient, time and cost saving alternative to the traditional methods of design and evaluation of potable water distribution networks.