Browsing by Author "Janhavi Singh"

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Air Quality and Human Health
(Springer Nature, 2023) Janhavi Singh; Swagata Payra; Sunita Verma
Air quality is a measurement that describes how good or poor air is present within the atmosphere. Good air contains a barely low amount of solid particles and chemical pollutants. Poor air consists of a high concentration of solid suspended particles along with gaseous pollutants, resulting in low visibility and damage to living organisms as well as the environment. Air pollutants, such as particulate matter and chemical pollutants (primarily ozone), disturb the energy balance of the planet, which directly influences or impacts climate in the worst ways. From an extremely local to the global level, the problem of degrading air quality has managed to leave its footprints all over the earth. As new epidemiological research became available, the consequences of air quality on human health became recognizable and rose to the top of the priority list by 2000. In 2019, the degradation of global air quality caused massive destruction over East Asia, Europe, and North America, taking away the lives of seven million people, extensive damage to crops, and a rapid reduction in biodiversity. Therefore, strong technical solutions and policies are needed to reduce the adverse effects of climate change. Policies developed for sustainable development of the environment globally as well as regionally can improve the condition of human health, vegetation quality and agriculture yield, which is degrading due to exposure to harmful pollutants. Recently, the clean air events at COP-27 also addressed the crucial role of air quality in climate change and human health and focused on the urgency of tackling air pollution in a global partnership. For all of these efforts to work, the enlightenment of the general public regarding degrading air quality and its impact is necessary. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
An analysis of particulate pollution using urban aerosol pollution island intensity over Delhi, India
(Springer Science and Business Media Deutschland GmbH, 2022) Janhavi Singh; Swagata Payra; Manoj K. Mishra; Sunita Verma
The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM10 and PM2.5) over Delhi. For analysis, the hourly concentration dataset of PM2.5 and PM10 from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM10, PM2.5, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM10 was observed to be 426.77 µg/m3 while that of PM2.5 was observed to be 301.91 µg/m3 in January 2019 for traffic-affected regions. During winters, higher PM2.5 concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM10 concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM10 pollution over Delhi. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Estimation of Particulate Matter (PM2.5) Over Kolkata
(Birkhauser, 2024) Janhavi Singh; Ajay Sharma; Shubha Shivani; Manoj Mishra; Sunita Verma
Particulate matter has a diverse range of effects on human health and climate due to which it has emerged as a key parameter in monitoring air quality. The current study explores and estimate the concentration of particulate matter (PM2.5) from MODIS AOD product over the city of Kolkata for a period of 3 years (2019–2021). PM2.5 concentration dataset was acquired from seven CPCB ground stations spread throughout Kolkata. Further, the study utilized the 1-km MODIS AOD product and meteorological parameters from MERRA-2. Considering the statistical analysis of data, four regression models were derived and considered for PM estimation. Daily estimated PM2.5 concentrations were compared against respective observations. The developed models were evaluated with the help of statistical methods. Model-2 based on the multi-linear regression equation was found to be the best fit model having a strong positive correlation between the estimated and observed PM2.5 values (R = 0.814). The root mean square error (RMSE) was estimated at 22.54 µg/m3. The estimated PM2.5 values were able to capture the trend of PM2.5 concentrations on the ground level. The normalized mean bias (NMB) value was − 0.315 and the mean absolute error was 18.94. The mean absolute percent error is estimated at around 5.16%. The results demonstrated that the developed model thus can be used to study the particulate matter concentration over areas where ground-based observation sites are sparse on the city level. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Fine particulate pollution and ambient air quality: A case study over an urban site in Delhi, India
(Springer, 2020) Janhavi Singh; Priyanshu Gupta; Deepak Gupta; Sunita Verma; Divya Prakash; Swagata Payra
Abstract: The current study discourses the impact of variation in PM2.5 concentration on the ambient air quality of Delhi. The 24-hourly PM2.5 concentration dataset was obtained from air quality measurement site (Anand Vihar) of Delhi Pollution Control Committee (DPCC) for the duration of April 2015 to December 2018. The annual and seasonal variability in the trend of ambient PM2.5 along with cumulative impact of meteorological parameters have been analyzed. The overall percentage increase in annual PM2.5 concentration, compared to National Ambient Air Quality Standards (NAAQS) guidelines, is observed to be 286.09%. The maximum concentration of fine particulate matter was recorded to be 788.6 µg/m3 during post-monsoon season and it was found to be associated with lower ambient temperature of 21.34°C and wind speed of 0.33 m/sec. Further, PM2.5 concentration was found to be correlated with CO (R = 0.6515) and NH3 (R = 0.6396) indicating similar sources of emission. Further, backward trajectory analysis revealed contribution in PM2.5 concentration from the states of Punjab and Haryana. The results indicated that particulate pollution is likely to occur in urban atmospheric environments with low temperatures and low wind speeds. Research highlights: PM2.5/PM10 ratio was observed to be highest in November, December and January, attributing aggravated levels of particle pollution to anthropogenic sources.Seasonal analysis of PM2.5 concentration indicated that particulate pollution was severe during post monsoon and winter months.Carbon monoxide (R = 0.6515; R2 = 0.4244) and Ammonia (R = 0.6396; R2 = 0.4088) were found to be correlated with PM2.5.Backward air mass trajectory depicted that air mass direction was coming to the receptor site (Anand Vihar) from the states of Haryana and Punjab. © 2020, Indian Academy of Sciences.
Impact of climate change and water quality degradation on food security and agriculture
(Elsevier, 2021) Priyanshu Gupta; Janhavi Singh; Sunita Verma; Amit Singh Chandel; Rajeev Bhatla
The gradual increase in climate change leads to a serious concern toward food security and agriculture production. Despite considerable progress, around 800 million people are malnourished, 161 million under age of five are considered obese and 2 billion do not receive the essential micronutrients in their healthy life. © 2021 Elsevier Inc. All rights reserved.
Impact of covid-19 on the air quality over china and india using long-term (2009-2020) multi-satellite data
(AAGR Aerosol and Air Quality Research, 2021) Manish Soni; Sunita Verma; Hiren Jethava; Swagata Payra; Lok Lamsal; Priyanshu Gupta; Janhavi Singh
We have examined the air quality over China, India and demonstrated marked differences in levels of air pollution resulted from the COVID-19 restrictions during December–April, 2019–20 to that of 11 years mean of 2009–19. The criteria air quality indicators i.e., nitrogen dioxide (NO2), sulphur dioxide (SO2), Aerosol Index (AI) and aerosol optical depth (AOD) data are retrieved from the Ozone Monitoring Instrument (OMI), TROPOspheric Monitoring Instrument (TROPOMI), and MODerate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra and Aqua satellites, respectively. Over China, during COVID-19 lockdown a significant drop in columnar abundances of tropospheric NO2 (–37%), SO2 (–64%) and AOD (–8%) for 2020 in comparison to 11 years mean (2009–19) has been observed. A noticeable difference in NO2 column burden is seen over SE (–35%), NE (–33%), NW (–13%) and SW (–5%) China. Over the SE and NE China, both NO2 and SO2 levels decreased dramatically in 2020 from that of 2009–19, by more than 40% and 65%, respectively, because of both stricter regulations of emissions and less traffic activity due to reduced social and industrial activities during COVID-19 restrictions. In contrast, the curve of monthly mean tropospheric columnar burden of NO2 and SO2 over India has shown moderate reduction of 16% and 20%, respectively because lockdown came into effect much later in March 2020. The mean NO2 and SO2 over IGP region is found to be 25% higher than whole India’s mean concentration due to large scale urban settlement and crop burning events. The statistical t-test analysis results confirm significant (p < 0.05) improvements in AQ during lockdown. The COVID-19 pandemic provided an unprecedented opportunity to investigate such large-scale reduction in emissions of trace gases and aerosols. Therefore, it is important to further strengthen environmental policies to tackle air quality, human health, and climate change in this part of the world. © The Author(s).
Rapid flash flood calamity in Chamoli, Uttarakhand region during Feb 2021: an analysis based on satellite data
(Springer Science and Business Media B.V., 2022) Sunita Verma; Ajay Sharma; Pramod Kumar Yadava; Priyanshu Gupta; Janhavi Singh; Swagata Payra
The present study investigates the accelerating factors for extreme flash flood at Chamoli district of Uttarakhand on 7 February 2021. The Sentinel-2A and 2B satellite data have been used to depict changes in pre-flood (16th of January) i.e., 5 years of 2016 to 2021 to post-flood (10 February, 2021) situation over the study domain. Vegetation and snow-cover from 2016 to 2021 has been obtained using Normalized Difference Vegetation Index (NDVI) classification over study area. Normalized Difference Water Index (NDWI) is used to extract the pre and post-flood water pixels for flood inundation mapping. The Cartosat-1 digital elevation model (DEM) product is used for drainage pattern and stream order mapping. Correlation between the meteorological parameters such as snowfall, wind speed and wind direction of Nanda Gunti peak during the time of flood with the flood event is analysed. The overall results indicate heavy snowfall (4.22 mm/day) over Nanda Gunti hills followed by high wind speed (23 km/hr.) that might have led to initiation of avalanche/landslide, giving rise to massive flash flood and eroded approximately 0.0263 km3 volume of landmass along with snow cover. Further, the 5 years NDVI analysis shows decrease in vegetation near Rishiganga and Alaknanda, a higher order river streams, is also crucial factor for flood intensification that caused massive destruction within the study area. The work highlights the importance of mapping of intense events and underline factors to reduce the impact and losses in case of future events. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Urban Air Quality Monitoring and Modelling Using Ground Monitoring, Remote Sensing, and GIS
(Springer Nature, 2023) Sunita Verma; Tanu Gangwar; Janhavi Singh; Divya Prakash; Swagata Payra
This chapter explores the advancements in urban air quality studies, focusing on the utilization of ground monitoring systems, remote sensing, and GIS techniques in urban air quality monitoring and modelling. It provides an overview of the importance of monitoring urban air quality, the challenges associated with it, and the need for comprehensive and integrated approaches to address this issue. This chapter highlights the role of ground monitoring stations, remote sensing technologies, and GIS in assessing and managing urban air pollution. It also discusses the application of these techniques in modelling air quality and predicting air pollutant concentrations. By integrating these techniques, researchers and practitioners can enhance their understanding of air pollution patterns, develop effective pollution control strategies, and promote sustainable urban development. The case studies and applications discussed in this chapter serve as valuable examples for decision-makers and environmental managers looking to improve air quality in urban areas. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Validation of Surface Temperature Derived From MERRA-2 Reanalysis Against IMD Gridded Data Set Over India
(Wiley-Blackwell Publishing Ltd, 2020) Priyanshu Gupta; Sunita Verma; R. Bhatla; Amit Singh Chandel; Janhavi Singh; Swagata Payra
The first detailed validation of maximum temperature of Modern-Era Retrospective analysis for Research and Application Version 2 (TMERRA-2) against Indian Meteorological Department (TIMD) has been carried out for 35 years (1981–2015) over India. For this purpose, India has been divided into seven different zones, i.e Western Himalaya (WH), Northwest, North Central, Northeast (NE), West Peninsula India, East Peninsula India, and South Peninsula India. The descriptive statistics and correlation between TMERRA-2 and TIMD have been determined for monthly, seasonal, and annual basis. A significant correlation (>0.9) has been found for monthly TMERRA-2 and TIMD with a root-mean-square error value closer to 1 except for WH where a high root-mean-square error value of 18.2 is obtained. Seasonal analysis also indicates a significant correlation for all the zones except for WH and NE with a correlation value of <0.3 during monsoon season; this may be due to sparse network, cold climate, and heterogeneity due to topography. Percent bias indicates that TMERRA-2 generally overestimates the TIMD monthly observations for all the zones, that is, Northwest, North Central, NE, West Peninsula India, East Peninsula India, and South Peninsula India by 4.1%, 2.4%, 1.6%, 0.5%, 0.2%, and 0.8%, respectively, except WH where an underestimation (−82.5%) is determined. Thus, after calibration, MERRA-2 Reanalysis maximum temperature may be used for further study of extreme weather events. © 2019. The Authors.