Browsing by Author "Pavan Kumar"

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A general approach to forest stand classification
(Elsevier, 2025) Megha Paul; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava; Pavan Kumar
The mapping of forests, evaluation of habitat quality, research into the dynamics of forests, and development of sustainable management techniques are only a few uses for forest typologies. The forest plots vertical and horizontal structures serve as the primary categorization standards in quantitative typologies designed for forestry applications. Forest typologies in which the univariate or bivariate distribution of tree diameters or heights is combined with species composition data to calculate coefficients that assess the dissimilarity of forest stands. One of the most important steps in planning forest management is classifying forest stands, but it takes time and is subject to subjectivity. The increasing availability of LiDAR data and multispectral photos presents an opportunity to enhance stand categorization using remotely sensed data. Using OBIA, forest stands have been automatically classified using ASTER images and low-density LiDAR data. In order to segment forests, OBIA was used in conjunction with VNIR ASTER bands to extract mean height, canopy cover, and the canopy model from LiDAR data. In order to compare the segmentation results, it was necessary to evaluate the internal heterogeneity of the segments. Multispectral information combined with OBIA and low-density LiDAR data are useful tools for stand classification. When it comes to distinguishing between broad-leaved, conifer, and mixed stands, multispectral pictures offer a limited predictive relevance for species distinction. However, the performance of ASTER data could be improved with higher spatial resolution VNIR images, especially submetric VNIR orthophotos. LiDAR data, however, has a lot of possibilities for depicting forest structure. The fast developing technology of drones and the increasing demand for high-resolution datasets from government agencies are factors that contribute to this perspective. © 2026 Elsevier Inc. All rights reserved..
Advanced Geospatial and Ground Based Techniques in Forest Monitoring
(Elsevier, 2025) Pavan Kumar; Prashant Kumar Srivastava; Mohammed Latif Khan; Ayyanadar Arunachalam; Partha Sarthi Roy; Kireet Vijay Sanil Kumar
Muthu Rajkumar, Parnika Gupta, ... Mohammed Latif Khan © 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies..
An overview of remote sensing technology in forest management
(Elsevier, 2025) Aishwarya; Meenu V.V.N.L.Sudha Rani; Preeti Kumari; Pankaj Lavania; Garima Gupta; Prabhat Tiwari; Ram Kumar Singh; Manoj Kumar; Manmohan J.R. Dobriyal; Manish Srivastav; Pavan Kumar
Remote sensing technology has revolutionized the field of forest management, offering unparalleled capabilities for monitoring, assessing, and managing forested landscapes. This overview paper explores the diverse applications and advancements of remote sensing techniques in forest management. It delves into the various remote sensing technologies, including satellite imaging, LiDAR (Light Detection and Ranging), and drones equipped with high-resolution cameras, highlighting their roles in data collection, analysis, and interpretation. This chapter discusses the utilization of remote sensing data for forest inventory, species identification, habitat assessment, and monitoring of forest disturbances such as wildfires, pests, and diseases. Furthermore, it emphasizes the integration of remote sensing with Geographic Information Systems (GIS) and machine learning algorithms for enhanced accuracy in mapping, modeling, and decision-making processes. Challenges and limitations inherent in remote sensing applications within forest management are also addressed, including issues related to data accuracy, processing techniques, and cost-effectiveness. Additionally, the paper explores future trends and potential advancements in remote sensing technology, emphasizing the need for continued research and development to further improve its efficacy in sustainable forest management practices. This chapter aims to provide a comprehensive understanding of the role and significance of remote sensing technology in modern forest management, emphasizing its potential to contribute to informed decision-making, conservation efforts, and the sustainable utilization of forest resources. © 2026 Elsevier Inc. All rights reserved..
Application of geospatial technology in agricultural water management
(Elsevier, 2020) Ram Kumar Singh; Pavan Kumar; Semonti Mukherjee; Swati Suman; Varsha Pandey; Prashant K. Srivastava
The geospatial technology is an emerging technique to study real earth geographic information using Geographical Information System (GIS), Remote Sensing (RS) and other ground information from various devices and instruments. In this chapter, various geospatial process-based techniques segregated into two different categories, i.e., conventional and advanced, are provided for agricultural water management. The descriptions of several approaches are provided to understand the role of geospatial technology in agricultural water management. Most of the approaches are based on remote sensing and GIS in correspondence with statistical learning techniques that can be possibly used for agricultural water management. © 2021 Elsevier Inc. All rights reserved.
Application of GPS and GNSS technology in geosciences
(Elsevier, 2021) Pavan Kumar; Prashant K. Srivastava; Prasoon Tiwari; R.K. Mall
Global Positioning System (GPS) is a global navigational satellite system developed by the United States Department of Defense. This technology is available only with America, Russia (GLONASS), China (BeiDou), and Japan (Quasi-Zenith Satellite System). In this, the navigation systems of America and Russia are global, while countries like China and Japan are using it regionally. The European Union has also completed preparations to start its navigation system. In terms of surveying, mapping technology, and engineering construction, it is used not only in the establishment of Earth control networks but also in the establishment of land and ocean geodetic survey benchmarks. Global Navigation Satellite System (GNSS) framework is one of the four major positioning systems, mainly GPS, GLONASS, GNS, and BeiDou, in the world. This chapter describes the application of GPS and GNSS Technology in Geosciences like rescue and relief projects, agriculture, dynamic observation, time transmission, speed measurement, vehicle guidance, and other fields. © 2021 Elsevier Inc. All rights reserved.
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.
Assessing soil organic carbon and its relation with biophysical and ecological parameters in tropical forest ecosystem India
(Taylor and Francis Ltd., 2025) Haroon Sajjad; Pavan Kumar; Prashant Kumar Srivastava; Shakti Om Pathak; Meraj Ahmed; Vikas Kumar; Manmohan J.R. Dobriyal; Preeti Kumari; Prem C. Pandey
Organic matter in soil is an essential parameter for assessing the agrodynamic productivity of soils. Forest productivity and health largely depend on soil organic carbon (SOC). This study aims to assess SOC levels and analyze their relationship with biophysical parameters in tropical forests. SOC was predicted using normalized difference vegetation index (NDVI) values derived from Sentinel-2A imagery. A total of 30 samples were collected through stratified random sampling based on NDVI values to estimate SOC. Regression analysis was performed between the estimated and predicted SOC, showing a strong correlation. The results indicated that SOC decreased with increasing soil depth in the Sariska Tiger Reserve, ranging from 8.27 - 26.54 t/ha at 5 cm depth and 1.9- 12.4 t/ha at 10 cm depth. NDVI was positively correlated with SOC, while the Bare Soil Index (BSI) showed a negative correlation. Additionally, soil pH and SOC were positively correlated, indicating high SOC levels. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Classification of forest stands based on tree crown morphology and dimensions
(Elsevier, 2025) Pavan Kumar; Hukum Singh; Manoj Kumar; Aishwarya; Narine Hakobayan; Manish Srivastav; Ram Kumar Singh
Accurately classifying forest stands is vital for effective forest management, ecological research, and conservation. This research explores the classification of forest stands based on tree crown morphology and dimensions, aiming to find a reliable method for categorizing forest stands through detailed analysis of tree crown characteristics. Tree crowns—the top part of trees composed of branches and leaves—serve as key indicators of forest structure and health. By examining various morphological features and dimensions of tree crowns, including their shape, size, and density, our study presents a refined classification system applicable to diverse forest ecosystems. This research not only enhances our understanding of forest stand dynamics but also provides practical tools for forest management and biodiversity preservation. © 2026 Elsevier Inc. All rights reserved..
Field screening of certain chickpea genotypes against gram pod borer, Helicoverpa armigera (Hübner)
(Malhotra Publishing House, 2022) Pavan Kumar; P.S. Singh; A.K. Saroj; G. Chaitanya; Ramkumar
Sixteen chickpea genotypes including susceptible Check BG 362 were screened against gram pod borer, Helicoverpa armigera. The mean larval population of H. armigera ranged from 0.69 to 3.22 larvae per five plants in different genotypes. The lowest larval population was recorded in genotype KPG 59 (0.69 larvae/five plants) and highest in genotype L 550 (3.22 larvae/five plants). The genotype RVG 203 recorded significantly lowest per cent pod damage (5.33%) followed by KPG 59 (6.03%) and BG 212 (9.66%). The genotype L550 recorded significantly highest per cent pod damage (31.33%) followed by BG 362 (24.33%) and GG 2 (23.33%). The genotypes RVG 203 (1840 kg/ha), KPG 59 (1822 kg/ha) produced highest grain yield. The genotype L 550 (8 PSR) recorded significantly highest pest susceptibility rating (PSR) and the lowest pest susceptibility rating was recorded in genotype KPG 59 and RVG 203 i.e 2 PSR © 2022, Journal of Entomological Research.All Rights Reserved.
Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India
(Springer International Publishing, 2019) Prem Chandra Pandey; Prashant K. Srivastava; Tilok Chetri; Bal Krishan Choudhary; Pavan Kumar
Forests are the potential source for managing carbon sequestration, regulating climate variations and balancing universal carbon equilibrium between sources and sinks. Further, assessment of biomass, carbon stock, and its spatial distribution is prerequisite for monitoring the health of forest ecosystem. Moreover, vegetation field inventories are valuable source of data for estimating aboveground biomass (AGB), density, and the carbon stored in biomass of forest vegetation. In view of the importance of biomass, the present study makes an attempt to estimate temporal AGB of Tripura State, India, using Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), leaf area index (LAI) and the field inventory data through geospatial techniques. A model was developed for establishing the relationship between biomass, LAI, and NDVI in the selected study site. The study also aimed to improve method for quantifying and verifying inventory-based biomass stock estimation. The results demonstrate the correlation value obtained between LAI and NDVI were 0.87 and 0.53 for the years 2011 and 2014, respectively. The correlation value between estimated AGB with LAI were found as 0.66 and 0.69, while with NDVI, the values were obtained as 0.64 and 0.94 for the years 2011 and 2014, respectively. The regression model of measured biomass with MODIS NDVI and LAI was developed for the data obtained during the period 2011–2014. The developed model was used to estimate the spatial distribution of biomass and its relationship between LAI and NDVI. The R2 values obtained were 0.832 for estimated and the measured AGB during the training and 0.826 for the validation. The results indicate that the methodology adopted in this study can help in selecting best fit model for analyzing relationship between AGB and NDVI/LAI and for estimating biomass using allometric equation at various spatial scales. The developed output thematic map showed an average biomass distribution of 32–94 Mg ha−1. The highest biomass values (72–95 Mg ha −1) was confined to the dense region of the forest while the lowest biomass values (32–46 Mg ha−1) was identified in the outer regions of the study site. © 2019, Springer Nature Switzerland AG.
Geospatial technology for water resource applications
(CRC Press, 2016) Prashant K. Srivastava; Prem Chandra Pandey; Pavan Kumar; Akhilesh Singh Raghubanshi; Dawei Han
This book advances the scientific understanding, development, and applicationof geospatial technologies related to water resource management. It presents recent developments and applications specifically by utilizing new earth observation datasets such as TRMM/GPM, AMSR E/2, SMOS, SMAP and GCOM in combination with GIS, artificial intelligence, and hybrid techniques. By linking geospatial techniques with new satellite missions for earth and environmental science, the book promotes the synergistic and multidisciplinary activities of scientists and users working in the field of hydrological sciences. © 2017 by Taylor & Francis Group.
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 geospatial technology for water resources
(CRC Press, 2016) Prem Chandra Pandey; Prashant K. Srivastava; Pavan Kumar; Akhilesh Singh Raghubanshi; Dawei Han
Increasing demands on water resources to ful ll the growing population needs have led to a great pressure on the water resources. Water resources conservation and management needs exemplary information regarding the water bodies with respect to quality, quantity and the related driving factors responsible for deterioration and depletion of water. Traditional methods existing in literature are limited to the point locations and manually gathered input dataset for analysis of the water system. However, after the development of advance geospatial technologies, now it is possible to build the digital information that can support analysis and interpretation for a large area in short span of time. e chapter introduces the various geospatial technologies, which are playing a vital and inevitable role in the acquisition of information and development of research capabilities towards water resources. ese technologies are required for determining a strategic plan for execution of desired results as applicable to di erent regions and objectives (for e.g. determination of water-river boundaries, water quality and quantity, soil moisture, ood plains, ocean temperature etc). is chapter provides di erent methods/applications to demonstrate the importance of traditional and advanced concepts of geospatial technology in water resources. us, overall goalof this chapter is to provide a summary of di erent research work carried out in various elds of water resources with demonstrated results and ndings that could be able to use in decisionmaking, developing policy and planning at root level. is chapter also provides future challenges in water resources andgeospatial technology. © 2017 by Taylor & Francis Group.
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.
Preface
(CRC Press, 2016) Prashant K. Srivastava; Prem Chandra Pandey; Pavan Kumar; Akhilesh Singh Raghubanshi; Dawei Han
[No abstract available]