Browsing by Author "Meenu Rani"

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Applications and challenges of geospatial technology: Potential and future trends
(Springer International Publishing, 2018) Pavan Kumar; Meenu Rani; Prem Chandra Pandey; Haroon Sajjad; Bhagwan Singh Chaudhary
This book advances the scientific understanding and application of space-based technologies to address a variety of areas related to sustainable development; including environmental systems analysis, environmental management, clean processes, green chemistry, and green engineering. Geo-spatial techniques have gained considerable interest in recent decades among the earth and environmental science communities for solving and understanding various complex problems and approaches towards sustainable technologies. The book encompasses several scopes of interests on sustainable technologies in areas such as water resources, forestry, remote sensing, meteorology, atmospheric and oceanic modeling, environmental engineering and management, civil engineering, air and environmental pollution, water quality problems, etc. The book will appeal to people with an interest in geo-spatial techniques, sustainable development and other diverse backgrounds within earth and environmental sciences field. © Springer Nature Switzerland AG 2019. All rights reserved.
Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology
(Nature Research, 2021) Ram Kumar Singh; Martin Drews; Manuel De la Sen; Prashant Kumar Srivastava; Bambang H. Trisasongko; Manoj Kumar; Manish Kumar Pandey; Akash Anand; S.S. Singh; A.K. Pandey; Manmohan Dobriyal; Meenu Rani; Pavan Kumar
The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections. © 2021, The Author(s).
Introduction to space technology challenges: Potential and future prospects
(Springer International Publishing, 2018) B.S. Chaudhary; Haroon Sajjad; Meenu Rani; P.C. Pandey; Pavan Kumar
This book affords an outline of the future overview of the current position and short-term insights into the space technology and the issues in the fast-mounting geospatial technology. A prosperous marker in the space journey from the traditional to advance remote sensing technology varying in space has been portrayed within objectives and outcomes. The usefulness of spectral bands with dissimilar spectral signatures provides vast data acquisition for application and services. Urbanization, dynamic nature of agriculture, land use planning, ocean exploration, vegetation resource management, and other ecosystems are being effectively monitored by the satellite services from the space and have many future prospects. Space technology assumes greater significance for monitoring natural and human resources and analyzing judicious utilization of resources. The technology provides standardized solutions for assessing potential and planning process in different geographical regions. Thus, space technology with its different services like geographical information system (GIS) and global positioning system (GPS) can effectively be utilized for timely analysis and future planning of resources on the planet Earth. The book is divided into 5 sections spreading over 16 chapters. The first section discusses the usefulness of geospatial technology in various fields. Chapters 2, 3, 4 and 5 of Part II are devoted to water resource and its various aspects. Natural hazard risk was assessed through various models and presented in Chaps. 6, 7, 8, and 9 of Part III. Part IV deals with progress and perspective scenario of urban growth models and covers Chaps. 10, 11, 12, and 13. Future challenges and prospects of geospatial technology have been examined in Chaps. 14, 15, and 16 of Part V. © Springer Nature Switzerland AG 2019. All rights reserved.
Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries
(Elsevier B.V., 2022) Martin Drews; Pavan Kumar; Ram Kumar Singh; Manuel De La Sen; Sati Shankar Singh; Ajai Kumar Pandey; Manoj Kumar; Meenu Rani; Prashant Kumar Srivastava
Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with “re-forecasts” produced by two of the most commonly used model types: (i) a compartment-type, susceptible–infected–removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods. © 2021 The Authors
Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night-Time Data
(Springer New York LLC, 2018) Bismay Ranjan Tripathy; Haroon Sajjad; Christopher D. Elvidge; Yu Ting; Prem Chandra Pandey; Meenu Rani; Pavan Kumar
Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993–2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r2 = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.
Multi-level impacts of the COVID-19 lockdown on agricultural systems in India: The case of Uttar Pradesh
(Elsevier Ltd, 2021) Pavan Kumar; S.S. Singh; A.K. Pandey; Ram Kumar Singh; Prashant Kumar Srivastava; Manoj Kumar; Shantanu Kumar Dubey; Uma Sah; Rajiv Nandan; Susheel Kumar Singh; Priyanshi Agrawal; Akanksha Kushwaha; Meenu Rani; Jayanta Kumar Biswas; Martin Drews
When on March 24, 2020 the Government of India ordered a complete lockdown of the country as a response to the COVID-19 pandemic, it had serious unwanted implications for farmers and the supply chains for agricultural produce. This was magnified by the fact that, as typically in developing countries, India's economy is strongly based on farming, industrialization of its agricultural systems being only modest. This paper reports on the various consequences of the COVID-19 lockdown for farming systems in India, including the economy, taking into account the associated emergency responses of state and national governments. Combining quantitative and qualitative sources of information with a focus on the Indian state of Uttar Pradesh, including expert elicitation and a survey of farmers, the paper identifies and analyzes the different factors that contributed to the severe disruption of farming systems and the agricultural sector as a whole following the lockdown. Among other issues, our study finds that the lack of migrant labor in some regions and a surplus of workers in others greatly affected the April harvest, leading to a decline in agricultural wages in some communities and an increase in others, as well as to critical losses of produce. Moreover, the partial closure of rural markets and procurement options, combined with the insufficient supply of products, led to shortages of food supplies and dramatically increased prices, which particularly affected urban dwellers and the poor. We argue that the lessons learned from the COVID-19 crisis could fuel the development of new sustainable agro-policies and decision-making in response not only to future pandemics but also to the sustainable development of agricultural systems in India and in developing countries in general. © 2020 Elsevier Ltd
Multi-temporal NDVI and surface temperature analysis for Urban Heat Island inbuilt surrounding of sub-humid region: A case study of two geographical regions
(Elsevier B.V., 2018) Meenu Rani; Pavan Kumar; Prem Chandra Pandey; Prashant K. Srivastava; B.S. Chaudhary; Vandana Tomar; Vinay Prasad Mandal
Rapid growing urban population has resulted in the occupancy of large proportionate of the city and its outskirts, thereby contributing factors to change in the environmental conditions. This has resulted in widespread land acquisition for built up and industrial development, covering the centre of the city while moving at the outskirts of the city as well. Land Use /Land Cover (LULC) changes causes alterations in the land use categories, mostly the concrete forests which has increased the urban temperature as compared to the rural regions due to rapidly growing urbanized environment. Urban Heat Island (UHI) is one of the human-induced environmental phenomenon affecting the urban inhabitant in many ways, such as altering and disturbing the land cover its use which changes thermal energy flow causing elevated surface and air temperature. Temporal satellite datasets (LANDSAT ETM+ image of 1989, 2000 and 2006) can be used to monitor surface temperature while vegetation indices can be used to assess the coverage of the vegetation and non-vegetation area in the region. Temporal NDVI is employed in the study area to analyse the impact of land surface temperature against NDVI in the region. Therefore, temporal remotely sensed data can be used to map LULC and its dynamic changes and other environmental phenomena such as surface temperature over a period of time. Temporal UHI has been estimated using geospatial technology to incorporate it for environmental impact assessment on the surrounding environment. The present research focuses on temporal NDVI and Surface temperature, the methodology used altogether for the assessment of resolution dynamic UHI change on environmental condition for Haridwar district, Uttrakhand India and Kanpur district, Uttar Pradesh in India. Both case study has different environmental conditions, geographical locations and demography. Hilly and forested region with almost no industrial activities for Haridwar while several industrial activities and densely populated region Kanpur located in an Indo-Gangetic plain. The research outcome demonstrates the correlation between temporal NDVI and Surface temperature exemplified with case study conducted over two different regions, geographically as well as economically. There is a need to consider the environmental dimension while making progress to urbanization. © 2018 Elsevier B.V.
Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor
(Springer Science and Business Media B.V., 2024) Pavan Kumar; Aishwarya; Prashant Kumar Srivastava; Manish Kumar Pandey; Akash Anand; Jayanta Kumar Biswas; Martin Drews; Manmohan Dobriyal; Ram Kumar Singh; Manuel De la Sen; Sati Shankar Singh; Ajai Kumar Pandey; Manoj Kumar; Meenu Rani
There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations. © The Author(s), under exclusive licence to Springer Nature B.V. 2023.
Short-term statistical forecasts of COVID-19 infections in India
(Institute of Electrical and Electronics Engineers Inc., 2020) Ram Kumar Singh; Martin Drews; Manuel de la Sen; Manoj Kumar; Sati Shankar Singh; Ajai Kumar Pandey; Prashant Kumar Srivastava; Manmohan Dobriyal; Meenu Rani; Preeti Kumari; Pavan Kumar
COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020. © This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/