Assessment of COVID-19 lockdown impact on surface water quality using remote sensing techniques in Raipur, Chhattisgarh, India

Due to improper management, industrialization and urbanization resulted in poorer surface and river water quality flowing through the city. Still, complete lockdown in the country resulted in improved surface water quality. Hence, a study has been performed to analyze these changes held during COVID-19 lockdown using a combination of different parameters derived from spatial data. The study includes analyses of significant water bodies, surface water bodies through out the city;the survey has proven that the lockdown situation that occurred due to the pandemic has resulted in improved water quality which has been determined based on water bodies analysis done for 12 major water bodies, and by the study performed it has been observed that the area of the nonturbid water has increased by 0.148 sq. km after the lockdown situation occurred. The study will be helpful to assess the impacts of lockdown on water bodies to take the sustainable measures which can be taken shortly for the improved regulation of pollutants and other contaminants based on positive effects on the surface water quality. © 2023 Elsevier Inc. All rights reserved.

Gis Based Spatial Analysis and Prediction of Covid-19 Cases

COVID-19 has come out to be a threat that has far-reaching repercussions in all parts of human existence;as a result, it is the most pressing concern facing countries around the world. This paper is centred on using a geographic information system to map COVID-19 instances across India, followed by COVID-19 case projections in various areas of India. A geographic information system (GIS) is a computer system that verifies, records, stores and displays data about places on the Earth’s surface, with India as the primary emphasis. Because the COVID-19 has had a distinct influence on different parts of India, the research we conducted provides a correct connection between past, current, and future instances in India employing prediction by using the SARIMA(Seasonal Autoregressive Integrated Moving Average) model to forecast time series. Python is used to implement the project. Several databases, including global databases like Natural Earth, UNEP Environmental Data Explorer, GRUMP, and national databases like Open Data Archive and ISRO’s Geo-Platform, are utilised to collect data for mapping and displaying instances across the country. These databases are combined to get the required output that is to be plotted and displayed. The prediction of coronavirus cases has also been done using the SARIMA model with an accuracy of 95.37percent.