Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfall data, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.

An investigation of the bio-medical waste produced in India during the COVID-19 pandemic and Maharashtra state (pre-COVID-19 and post-COVID-19) analysis: a GIS-based approach.

BACKGROUND: The COVID-19 pandemic exerted substantial pressure on global healthcare systems and facilities, putting the lives of countless individuals at risk. In addition, the treatment of patients during the pandemic resulted in an unprecedented increase in the volume of medical waste generated, including biomedical waste (BMW) or healthcare waste (HCW), which poses a risk of infectious disease transmission. As the second most populous country in the world, India faced a severe challenge in managing its healthcare waste infrastructure during this time (2020-2021). Proper disposal of BMW was of utmost importance to prevent the spread of infectious agents and to safeguard public health. METHODS: The environmental monitoring and management framework of the country is well planned and governed by the Central Pollution Control Board (CPCB), which carefully handles the BMW across the states and union territory of the country. Through the execution of Android based application named 'COVID19BMW', India has laid the foundation of identification, classification, data collection, and management regarding the BMW. Further, the temporal scale of BMW generation tracking was further improved from a monthly to a daily basis by using the COVID19BMW tool. This data was used to map the change taken place across the India. Additionally, by using Geographical Information System the BMW is mapped using Choropleth method. RESULTS: The current study conducted a national-level analysis of BMW generated during the COVID-19 pandemic in India. The results revealed that, in the year 2020, the seven states and the National Capital Territory (NCT) of Delhi generated the highest amounts of BMW, with Gujarat, Maharashtra, Kerala, Karnataka, Tamil Nadu, Uttar Pradesh, and West Bengal being the top BMW generating states. Additionally, the change detection equation was used to map the changes. The investigation analysed the daily changes in BMW generation between 2020 and 2021 at the national level. The results indicated a significant decreasing trend of -50.35% in BMW generation per day. In the case of Maharashtra state, the change detection analysis for the pre-COVID-19 and post-COVID-19 pandemic periods showed an increased trend of approximately 32%. However, in 2021, a decreasing trend was observed, with a -2.23% reduction in BMW generation compared to 2020 on the daily basis of BMW generation. These findings suggest that the COVID-19 pandemic has influenced BMW generation of waste, and the results can provide insights for improving waste management policies and practices. DISCUSSION: In this study, a Geographical Information System (GIS) was employed to create a mapped representation of the BMW data at national scale. Further, the study investigated changes in BMW generation in Maharashtra state during the COVID-19 pandemic. Analysis of changes in BMW generation revealed a pattern of BMW generation during the pandemic. The use of GIS technology to track these changes proved to be a valuable tool in providing a synoptic view of the overall BMW condition across India and identifying areas where infectious waste poses a significant threat. The visualisation of data using the GIS technique provided an easy means of identifying hotspots of BMW generation, which is more effective compared to a tabular format.