Browsing by Author "Manish Soni"

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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
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
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.
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).
Particulate matter estimation over a semi arid region Jaipur, India using satellite AOD and meteorological parameters
(Elsevier B.V., 2018) Manish Soni; Swagata Payra; Sunita Verma
The present study estimates ground-level Respirable Particulate Matter (RSPM) by the combined use of satellite remote sensing Aerosol Optical Depth (AOD) at 550 nm (AODMODIS or MODIS AOD) and ground-based meteorological measurements from April-2010 to March-2014 over Jaipur, semi-arid region in North-western, India. The satellite MODIS Level 2.0 AOD is used in developing multi-regression statistical models to estimate RSPM values over the study area. The relationship between particulate matter (PM) and AOD depends on size distribution, particle composition and vertical profile of aerosols. Thus, for optimal representation of MODIS AOD, the factors like Height of Planetary Boundary Layer (HPBL) and meteorological parameters has been considered in all regression models in the present study as surrogates. The performance of regression models is analyzed on the basis of descriptive statistical measures i.e. Normalised Mean Square Error (NMSE), Correlation (R), Factor of two observations (FA2), and Fractional Bias (FB). The nonlinear multi-regression model (MODEL V) performed better than other models for our study period and region on the basis of statistical analysis (R = 0.80, NMSE = 0.01, FB = 0.0, FA2 = 100). The coefficients obtained from MODEL V were again used over Jodhpur and found to perform better than other models. The study is further extended to find out the Air Quality Index (AQI) category over Jaipur. The average RSPM obtained from Rajasthan Pollution Control Board (RPCB) observations and those of model estimated values come under the “Moderately Polluted” category as per Indian air quality standards. © 2018 Turkish National Committee for Air Pollution Research and Control
The major lightning regions and associated casualties over India
(Springer, 2020) Pramod Kumar Yadava; Manish Soni; Sunita Verma; Harshbardhan Kumar; Ajay Sharma; Swagata Payra
Lightning, a climate-related highly localized natural phenomenon, claims lives and damage properties. These losses could only be reduced by the identification of active seasons and regions of lightning. The present study identifies and correlates the lightning-prone regions with the number of casualties reported over India at the state/union territory level. The seasonal and monthly composite satellite data of Lightning Imaging Sensor for the duration of 16 years (1998–2013) have been analyzed in this study for the identification of the major lightning-prone seasons and regions over India. The casualties due to lightning have also been estimated using data from Accidental Deaths and Suicides in India, National Crime Record Bureau report of India. The spatial distribution analysis reveals that lightning occurs mostly in hilly regions over India throughout the year (26 flash/sq. km/yr) and, however, causes lesser casualties because of the sparse population over the hilly terrain. The seasonal analysis reveals the most lightning phenomena occur during the pre-monsoon period (40–45 flash/sq. km/yr) over the northeast region of India. During the winter period, the lightning dominates over the northern parts of India such as Jammu and Kashmir. The state-wise casualties’ study reveals that maximum casualties are reported in Madhya Pradesh (313 deaths), Maharashtra (281 deaths) and Orissa (255 deaths) on an average per annum. The favorable climatic conditions, such as availability of moisture content, unstable atmosphere and strong convection, cause severe cases of lightning over the regions of Orissa and Maharashtra. © 2020, Springer Nature B.V.