Browsing by Author "Sunita Verma"

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Aerosols properties over desert influenced locations situated in four different continents
(Elsevier Ltd, 2021) Manish Soni; Amit Singh Chandel; Sunita Verma; Swagata Payra; Divya Prakash; Brent Holben
This paper investigates aerosol properties (physical, optical and radiative) to understand the aerosol climatology (2008–17) over four Aerosol Robotic Network (AERONET) sites situated in different continents. For this purpose, the chosen sites are Jaipur, India in Asia, Ilorin, Nigeria in Africa, Birdsville, Queensland in Australia and White Sand, New Mexico in America. The higher AOD were found at Jaipur (AOD≈0.57; α = 0.38) during month of June and at Ilorin (AOD ≈ 1.12 and α ≈ 0.56), Africa during February. The value of SSA are also found higher i.e. 0.94 and 0.96 during the MAM over Jaipur and Ilorin respectively due to dominance of dust aerosols. Ilorin experiences the influence of harmattan winds from November to March and shows significant increase not only in coarse mode but also in fine particles fraction. While the remaining sites i.e. White Sand, America; and Birdsville, Australia are found relatively pristine based on monthly averaged AOD, AE (α) and SSA. The estimated direct radiative forcing using SBDART indicates that Ilorin and Jaipur sites in Africa-Asia exhibit much higher values of TOA and BOA as compared to White Sands and Birdsville in America-Australia. The annual averaged radiative forcing are estimated over Ilorin (38.38 ± 16.89 W m−2) and Jaipur (36 ± 8.34 W m−2). Similarly, high radiative forcing efficiency of 66.86 ± 16.69 W m−2 τ0.55nm−1 and 67.96 ± 20.46 W m−2 τ0.55nm−1 are calculated for Ilorin and Jaipur, respectively. The influence of emission differs in different continents i.e. Africa-Asia to America-Australia sites. © 2021 Elsevier Ltd
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.
An evaluation of inter and intra population structure of Uttar Pradesh, inferred from 24 autosomal STRs
(Taylor and Francis Ltd., 2022) Ikramul Haque; Shivani Dixit; Akash Kumar; Akshay Kumar; Sunita Verma; Devinder Kumar; Ankit Srivastava; R.K. Kumawat; Divya Shrivastava; Gyaneshwer Chaubey; Pankaj Shrivastava
Aim: The present study was designed to explore the STR diversity and genomic history of the inhabitants of the most populous subdivision of the country. A set of 24 hypervariable autosomal STRs was used to estimate the genetic diversity within the studied population. A panel of 15 autosomal STRs, which is most common in the previously reported data sets, was used to estimate the genetic diversity between the studied population, and obtained unique relations were reported here. Method: The genetic diversity and polymorphism among 636 individuals of different ethnic groups, residing in Bareilly, Pilibhit, Shahjahanpur, Gorakhpur, Jhansi, and Varanasi regions of Uttar Pradesh, India, was investigated. This investigation was carried out via 24 autosomal STRs. Result: The 24 loci studied showed the highest value of combined power of discrimination (CPD = 1), combined power of exclusion (CPE = 0.99999999985), combined paternity index (CPI = 6.10 × 109) and lowest combined matching probability (CPM = 7.90 × 10−31). Conclusion: The studied population showed genetic closeness with the population of Uttarakhand, the Jats of Delhi,the Jat Sikh (Punjab), and the population of Rajasthan. Among the tested loci, SE33 and Penta E were found to be most useful in terms of the highest discrimination power, lowest matching probability, the highest power of exclusion, and highest polymorphism information content for the Uttar Pradesh population. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
An evaluation of long-term gridded datasets of total columnar ozone retrieved from MERRA-2 and AIRS over the Indian region
(Springer Science and Business Media Deutschland GmbH, 2023) Priyanshu Gupta; Swagata Payra; R. Bhatla; Sunita Verma
Accurately determining the spatiotemporal variability of ozone on a regional to intercontinental scale is essential for air quality studies. In the present study, a first systematic evaluation and analysis of long-term (2009–2020) gridded datasets (0.5° × 0.625°) of total columnar ozone (TCO) retrieved from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2TCO) is evaluated for the Indian region. The MERRA-2TCO is first validated with observations (IMDTCO) and then further compared with the Atmospheric Infrared Sounder (AIRSTCO) satellite datasets. For an in-depth comparison and statistical analysis, the dataset has been segregated into seven distinct regions, i.e., Western Himalaya (WH), North East (NE), North Central (NC), North West (NW), West Peninsula India (WPI), East Peninsula India (EPI), and South Peninsula India (SPI). Descriptive statistics (NMSE, FB, R, FA2, and d) reveals a significant correlation of MERRA-2TCO against IMDTCO for Delhi with NMSE (0.0013), FB (− 0.029) and Varanasi NMSE (0.0008), FB (− 0.014). The results of simple linear regression analysis show an increasing TCO trend value of 0.31% and 0.44% per decade in both the cities, respectively. A comparison of MERRA-2TCO with AIRSTCO shows a significant correlation of 0.62–0.87 in different regions of India. Furthermore, in support of Brewer’s circulation pattern, an increasing shift of columnar ozone from low (SPI) to high (WH) latitudinal regions is observed. Our results show that the MERRA-2 ozone dataset can be effectively used for ozone air quality studies over India and this analysis may strengthen the need for independent, high-quality, and consistent ozone measurements with small uncertainties. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Application of remote sensing to study forest fires
(Elsevier, 2022) Swagata Payra; Ajay Sharma; Sunita Verma
Forest fire is one of the most common disaster that takes place in many forest systems throughout the globe. Forest fire has a devastating impact over the environment, landscape, and ecological succession. Every year entire globe witnesses a large number of forest fires. In the foothills of the Indian Himalayan region (Shivalik), periodical spatial and severity of forest fires varies, as fires in these areas are often associated with a large concentration of pine needles on the forest floor. Thereby, forest fire assessment and mapping is very important to minimize the effect and frequency of fire events. Monitoring forest fire over large area has become cost and time effective by using remote sensing imageries (spaceborne or airborne). Remote sensing is one of the most important tools to study and detecting forest fires in cases. Global as well as periodic coverage of remote sensing data have replaced the traditional methods of fire detection to a huge extent. Remote sensing approaches help to analyze wide scenario and factors that affect forest fire. Availability of a wide range of fire detection sensors like MODIS, VIIRS, Sentinel, and so on provide us with plenty of options for mapping forest fire severity and provide mitigation. Various indices like normalized burned ratio, normalized burned thermal ratio, burned area index are most commonly used for forest fire severity mapping. The present chapter provides an overview on advancements in remote sensing techniques which can be used to map the fire incidence. © 2023 Elsevier Ltd. All rights reserved.
Assessment of bias correction technique to improve ozone reanalysis dataset over India
(Springer Nature, 2025) Tanu Gangwar; Anumeha Dube; V. Abhijith; Sunita Verma
This study provides the first systematic evaluation of three global reanalysis ozone products MERRA-2, CAMS and ERA5 against quality-controlled CPCB ground observations over five climatological regions of India. This study has two primary objectives: (1) to document the performance of these datasets across India’s geographically, climatologically and demographically diverse regions; and (2) to evaluate the effectiveness of various bias-correction methods in improving their agreement with observations. Observed daily mean ozone concentrations ranged from 6.7 to 57.6 µg/m3; all three-reanalysis exhibited regionally coherent biases, with CAMS most prone to overestimation (mean bias: 42.3 to 108.1 µg/m3). Spatial patterns of bias varied by region, with the largest positive departures over the Indo Gangetic Plains (IGP), Western India (WI), Himalayan Region (HR), Central India (CI) and Southern India (SI). Verification metrics like RMSE, MAE, correlation coefficient (r), index of agreement (d) is used to analyse the strengths and weaknesses of each dataset in capturing ozone variability over these regions. To enhance the dataset accuracy bias correction techniques, including Quantile–Quantile (QQ) mapping, Ratio Adjustment Transformation (RAT-add and RAT-multi), and Variance Scaling (Vari), were applied. The RAT-multi method emerged as the most effective, substantially reducing F-Bias, RMSE, and MAE while improving correlation (r) and Index of agreement (d). Notable improvements were observed in CI and IGP, where Corrected MERRA-2 achieved an RMSE of 17.164 µg/m3 and F-Bias ~ 1. In the IGP region, the CAMS ozone dataset was corrected using the RAT-multi method showed statistically significant performance, by achieving improvement of 75.647%. This was followed by WI (72.080%), SI (69.358%), HR (67.313%), and CI showed the least improvement with 65.257%. Challenges persisted in the Himalayan Region due to its complex topography. This study establishes a benchmark for bias correction of reanalysis datasets over India, with corrected CAMS using RAT-multi outperforming others. This study underscores the importance of post-processing reanalysis data to address biases arising from limitations in model physics and parametrization, thereby improving its applicability for regional air quality assessments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Assessment of extreme rainfall events over Kerala using EVA and NCUM-G model forecasts
(Springer, 2023) V. Abhijith; Raghavendra Ashrit; Anumeha Dube; Sunita Verma
Assessment of extreme rainfall events (ERE) is crucial for disaster management. Numerical weather prediction (NWP) model-based predictions often fail to predict the extremes. This could be due to several reasons, including insufficient model resolution to capture the sub-grid scale processes, inadequate high-quality observational data for assimilation, uncertainty in initial conditions and approximations in model physics. Estimation of rainfall for different return periods (RP) using extreme value analysis (EVA) can aid in better decision-making. RP of an event indicates its probability and rarity over the region. The current study shows how EVA can be used to supplement model predictions. This study uses the high-resolution (0.25×0.25) gridded observed rainfall data from India Meteorological Department (IMD), which has been available for 117 years (1901–2017). The generalised extreme value (GEV) distribution is applied with suitable goodness-of-fit tests. Rainfall amounts corresponding to 100-year RP are estimated using EVA over the entire data period (1901–2017) and three epochs (1901–1940, 1941–1980, and 1981–2017). The results indicate increasing rainfall amounts corresponding to 100-year RP. Similarly, rainfall amounts for 25, 50, 100, and 200-year RP over Kerala are computed to compare with the extremely heavy rainfall (≤21 cm/day) amounts reported during JJAS 2018 and 2019. Further, this approach supplements the operational forecasts of NCUM-G model forecasts. © 2023, Indian Academy of Sciences.
Changes in tropospheric ozone concentration over Indo-Gangetic Plains: the role of meteorological parameters
(Springer, 2022) Swagata Payra; Priyanshu Gupta; Abhijit Sarkar; R. Bhatla; Sunita Verma
This study seeks to understand and quantify the changes in tropospheric ozone (O3) in lower troposphere (LT), middle troposphere (MT) and upper middle troposphere (UMT) over the Indo-Gangetic Plains (IGPs), India during the COVID-19 lockdown 2020 with that of pre-lockdown 2019. The gridded datasets of ozone from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis product, ERA5 in combination with statistical interpolated (IDWs) surface NO2 observations, present a consistent picture and indicate a significant tropospheric ozone enhancement over IGP during COVID-19 lockdown restrictions in May 2020. The Paper also examines the influencing role of meteorological parameters on increasing ozone concentration. Over LT, an increase in O3 concentration (23%) is observed and in MT to UMT an enhancement of about 9–18% in O3 concentration have been seen during May 2020 with respect to May 2019. An investigation on causes of increasing ozone concentration (35–85 ppbv) from MT to UMT during May 2020 reveals that there was significant rise (by 1–6%) in low cloud cover (LCC). Notably, higher LCC increases the backscattering of upward solar radiation from the top of the atmosphere. A positive difference of 5–25 W/m2 in upward solar radiation (USR) is observed across the entire study region. The result suggests that higher LCC significantly contributed to the enhanced USR. Thereby, resulting in higher photolysis rate that lead to an increase in mid tropospheric ozone concentration during May 2020. The results highlight the importance of LCC as an important pathway in ozone formation and aid in scientific understanding of it. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
Characterizing aerosols during forest fires over Uttarakhand region in India using multi-satellite remote sensing data
(Elsevier Ltd, 2022) Sunita Verma; Manish Soni; Harshbardhan Kumar; Swagata Payra; Manoj K Mishra; Rohini Bhawar
Confirmed rise in average surface temperature and consequent prolonged dry days in tropical Himalayan foothills (tarai region) favors frequent wildfire event which make susceptible to the local forest vegetation and ecology. Recent improvement in spatio-temporal resolution of space-borne sensors provides an opportunity to routinely map these wildfires and estimate the consequence. Utilizing both active and passive space-borne multi-sensors, this study presents the active fire counts, columnar and vertical distribution of aerosol during 2013–2018 over Uttarakhand region of India. Our analysis shows maxima in active fire counts during April to late June months while minima in monsoon over the region. Particularly owing to the high temperature, low moisture, drying up of natural spring and availability of fuel materials in summer and scare precipitation in winter. Some limited spatio-temporal scale fire episodes are also marked in winter. The AOD values with maximum of 3.2 (0.5 mean) observed during the April 2016, while for the successive next two months, AOD of 2.0 and 1.2 are found over the fire burning regions. The Normalized Burn Ratio Thermal (NBRT) index are also found to be much higher for April and May 2016 with respect to 2015 and 2017. The comparative analysis of NBRT shows a positive difference towards the western side of Uttarakhand. Vertical feature mask and aerosol subtype profile details about the polluted dust and elevated smoke aerosols from surface to 10 km range during the intense fire events smoke were elevated and trapped within 3 to 10 km. The results demonstrate the potential of earth's observing satellites for characterization of emissions and in air quality management. © 2022 COSPAR
Emission Sources of Particulate Matter
(Springer Nature, 2022) Swagata Payra; Preeti Gunwani; Sunita Verma
Particulate matter is recognised to have profound effect on environment and climate changes. It is essential that the sources of particulate matter in atmosphere are well understood and quantified to provide a sound basis for formulating policies for the reduction in anthropogenic influences on climate. For the formulation of preliminary estimate of particulate matter burden and impacts, it is necessary to know the relative importance of generation of these particles and the relevant pathways in atmosphere. This chapter provides an overview on emission sources of particulate matters in the atmosphere. The chapter also details the optical and radiative properties. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
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.
Estimation of particulate matter pollution using WRF-Chem during dust storm event over India
(Elsevier B.V., 2022) Manish Soni; Sunita Verma; Manoj K. Mishra; R.K. Mall; Swagata Payra
This study estimates the ground-level PM10 concentration by effectively combining the Aerosols Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite retrievals and Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The model simulates typical dust storm events 17th-22nd April 2010 and 05th–10th May 2010 which has severely affected air quality in North and Northwestern India. The satellite retrievals shows high AOD (>1.0) over Indo Gangetic Plains and nearby Thar Desert. The model captures the spatial pattern of AOD very well however, it underestimates high aerosol loading in comparision to MODISAOD. The modeled AOD (MODELAOD) shows an underestimation by 37% with MODISAOD over the study region. Therefore, the WRF-Chem model Particulate Matter (PM10) and MODELAOD are scaled using satellite MODISAOD to provide a better estimation of the particulate pollution. The results shows better estimation, trend and correlation (R = 0.83) of the PM10 with hourly observations at Delhi monitoring station and a Mean Bias (MB) of 61 μg/m3 during the satellite overpass time. The comparison of estimated PM with daily averaged observations of PM10 from Central Pollution Control Board (CPCB) at stations of Jaipur, Jodhpur, Kota, and Delhi showed a strong agreement with an correlation of (R) of 0.81, 0.70, 0.77 and 0.78, respectively. © 2022 Elsevier B.V.
Evaluation of MERRA-2 Total columnar ozone from ground based and AIRS satellite product
(Institute of Electrical and Electronics Engineers Inc., 2022) Priyanshu Gupta; Sunita Verma; Swagata Payra; R. Bhatla
In the present study, a first systematic evaluation and analysis of long-term (2009-2020) gridded datasets (0. 5° × 0.625) of total columnar ozone (TCO) from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2TCO) is carried out over the Indian subcontinent. The MERRA-2TCO is first validated with observed (IMDTCO) and then further compared with Atmospheric Infrared Sounder (AIRSTCO) satellite dataset. For an in-depth comparison and statistical analysis, the dataset has been segregated into seven distinct regions, i.e. Western Himalaya (WH), Northeast (NE), North Central (NC), Northwest (NW), West Peninsula India (WPI), East Peninsula India (EPI), and South Peninsula India (SPI). Descriptive statistics (NMSE, FB, R, FA2 and d) reveals significant correlation of MERRA-2TCO with IMDTCO over Delhi (NMSE=0.0013, FB=-0.029) and Varanasi (NMSE=0.0008, FB=-0.014) cities. Further, a comparison of MERRA-2TCO with AIRSTCO represents the NMSE values ranging from 0.0023-0.0047 DU and a correlation coefficient of 0.62-0.87 in different regions of India. In support of Brewer's circulation pattern, an increasing strong shift of columnar ozone from low (SPI) to high (WH) latitudinal regions in annual variation (2009-2020) is observed. Our finding indicate that the MERRA-2 ozone dataset can be effectively used for ozone air quality studies over the Indian regions and the analysis may highlight the necessity for independent, reliable, consistent, and accurate ozone observations. © 2022 International Radio Science Union (URSI).
Fidelity of WRF model in simulating heat wave events over India
(Nature Research, 2024) Priyanshu Gupta; Sunita Verma; Parthasarathi Mukhopadhyay; R. Bhatla; Swagata Payra
The evaluation of Weather Research and Forecasting (WRF) model has been performed for simulating episodic Heat Wave (HW) events of 2015 and 2016 with varied horizontal resolutions of 27 km for the entire India (d01), 9 km for the North West (NW (d02)) and South East (SE (d03)) domain. Study compares the maximum temperature (Tmax) simulated by WRF model, using six different combination of parameterization schemes, with observations from the India Meteorological Department (IMD) during the HW events. Among the six experiments, Exp2 (i.e., combination of WSM6 microphysics (MP) together with radiation parameterization CAM, Yonsei (PBL), NOAH land surface and Grell-3D convective schemes) is found closest to the observations in reproducing the temperature. The model exhibits an uncertainty of ± 2 °C in maximum temperature (Tmax) for both the regions, suggesting regional temperature is influenced by the location and complex orography. Overall, statistical results reveal that the best performance is achieved with Exp2. Further, to understand the dynamics of rising HW intensity, two case studies of HW days along with influencing parameters like Tmax, RH and prevailing wind distribution have been simulated. Model simulated Tmax during 2015 reaches up to 44 °C in NW and SE part of India. In 2016, HW is more prevailing towards NW, while in SE region Tmax reaches upto 34–38 °C with high RH (60–85%). The comparative research made it abundantly evident that these episodic events are unique in terms of duration and geographical spread which can be used to assess the WRF performance for future projections of HW. © 2024, The Author(s).
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.
Heatwaves Over the Indian Subcontinent: Mechanisms, Variability and Sources
(Springer Science and Business Media Deutschland GmbH, 2025) Priyanshu Gupta; Aditya Kumar Dubey; R. Bhatla; Swagata Payra; Sunita Verma
Heatwaves, an extreme temperature events, have gained copious attention. This study conducts a comprehensive analysis of heatwaves (HWs) over four decades from 1980 to 2021. Empirical Orthogonal Function (EOF) analysis is used to identify the dominant spatial pattern in maximum temperature (Tmax) variability across the Indian region. The analysis identifies four significant regions: Central Northeast (CNE), South Central (SC), Northwest (NW) and Southeast (SE) with distinct temperature pattern. Subsequently, the spatial and temporal variations in HWs, indicate an increase in frequency, severity, and duration over the past four decades. Temporal trend shows an increase in HW frequency and duration across most of the regions over a 42-year period. To comprehend the underlying mechanism of HW events, study investigates various meteorological parameters observed during HW days. Maximum temperature anomalies increase by 4–5 °C, accompanied by positive surface solar radiation (SSR) of 10–20 W/m2 along with lower mean sea level pressure (mslp), and a reduction in relative humidity (RH) of − 6% to -15%. Geopotential height at different pressure level highlight anticyclonic circulation pattern associated with temperature extremes. Additionally, backward trajectory analysis is used to delineate distinct source regions and atmospheric processes linked with HW events in different parts of India. A detailed analysis of spatial and temporal variation of HWs and their associated meteorological factors contribute in development of effective mitigation and adaptation strategies. © King Abdulaziz University and Springer Nature Switzerland AG 2025.
Impact of an annular solar eclipse on trace gases and meteorological parameters over Jaipur, Northwestern India
(Frontiers Media S.A., 2022) Divya Prakash; Sunita Verma; Swagata Payra; Vivek Kumar
This study aimed to identify the impact of an annular solar eclipse i.e., 21 June 2020 on the variation of meteorological parameters along with trace gases using statistical analyses. The study site is located at Poornima University, Jaipur (26.7796°N, 75.8771°E), Rajasthan, India. The observational analysis indicates a rapid decrease in solar direct radiation (SDR) which varied between 706 and 79 W/m2 during the eclipse. SDR was reduced to 79 W/m2 at the maximum peak of the solar eclipse at 11:55 a.m. at the study location. The comparative analysis shows the variation of SDR during the solar eclipse day, the previous day, and the day after the event. A strong dip was observed in SDR during the annular eclipse day concerning before (734.31 W/m2) and after (734.375 W/m2) eclipse event. Furthermore, the impact of the solar eclipse on temperature (Ts) and Relative Humidity (RH) was analyzed over Jaipur. The statistical analyses demonstrate an apparent decrease in temperature of about 2°C while RH shows a slight increment (3.45%) during the solar eclipse event. The results show an inverse correlation between the solar eclipse and trace gases variations during the eclipse due to the changes in solar radiation, surface temperature, and variation in winds that might affect the photochemical processes. Copyright © 2022 Prakash, Verma, Payra and Kumar.
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 climate change on water quality and its assessment
(Elsevier, 2022) Pramod Kumar Yadava; Harshbardhan Kumar; Anubhuti Singh; Vinod Kumar; Sunita Verma
Worldwide changes in climatic conditions are encouraged by increase in the concentration of Green House Gases (GHGs), are widely perceptible in terms of continuous changing patterns of superficial temperature, rainfall pattern, wind-flow patterns, radiations, and other life-threatening weather conditions. With a wider consent from the regional and global scientific communities, Intergovernmental Panel on Climate Change (IPCC) has indisputably summarized that kind of fact. Therefore, in this ongoing era of global climate-change, the assessment of probable impact on water and its quality, being the most prominent and reliable resource for human existence, become a significant task. Lots of assessment and review study have been reported on the possible impact of climate-change on water cycling and precipitation pattern. Recently, in-situ quality assessment, satellite observational assessment, and modeling simulation studies are in progress to assess the possible impact of climate change on the quality of available water. These studies mostly reported that the rising temperature and hence changing climatic conditions are capable of varying the ecological balance of water as well its qualitative contents either by its indirect impact or by direct impact by performing various biochemical alternations, as the rise in the water temperature and changes in extremes like flash-flood worsen various reasons of pollution caused in water bodies. In a natural water system, sediment load, nutrients availability, dissolved organic carbon, and essential zoo planktonic community are found to be in a delicate balance. Change in the flow pattern and thus augmentation in nutrients concentration, predictable variation in climatic phenomenon leads to periodic phytoplankton blossoms and alteration of the ecological tropical balance. The resultant dissolved oxygen (DO) level is varied constantly and algal-blooms may range to the perilous level to affect negatively. In addition, melting glaciers and consequent rising levels of the sea are expected to encompass the zones of salinization toward freshwater resources, which results in the reduction in available freshwater resources in the coastal areas. Additionally, variations in the qualitative value of water are predictable to affect nutriment accessibility, steadiness, access, and consumption. All these detrimental effects of changing climate hence water quality can adversely impact food security and hence enhancing the vulnerability of the agriculturalists and civilizations of our rural culture surviving in arid regions like Asian and African deltas (IPCC, Technical report on climate change and water, June 2008). © 2023 Elsevier Inc. All rights reserved.