Browsing by Author "Umesh Chandra Dumka"

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Chemical composition and source apportionment of total suspended particulate in the central himalayan region
(MDPI, 2021) Rahul Sheoran; Umesh Chandra Dumka; Dimitris G. Kaskaoutis; Georgios Grivas; Kirpa Ram; Jai Prakash; Rakesh K. Hooda; Rakesh K. Tiwari; Nikos Mihalopoulos
The present study analyzes data from total suspended particulate (TSP) samples collected during 3 years (2005–2008) at Nainital, central Himalayas, India and analyzed for carbonaceous aerosols (organic carbon (OC) and elemental carbon (EC)) and inorganic species, focusing on the assessment of primary and secondary organic carbon contributions (POC, SOC, respectively) and on source apportionment by positive matrix factorization (PMF). An average TSP concentration of 69.6 ± 51.8 µg m−3 was found, exhibiting a pre-monsoon (March–May) maximum (92.9 ± 48.5 µg m−3) due to dust transport and forest fires and a monsoon (June–August) minimum due to atmospheric washout, while carbonaceous aerosols and inorganic species expressed a similar seasonality. The mean OC/EC ratio (8.0 ± 3.3) and the good correlations between OC, EC, and nss-K+ suggested that biomass burning (BB) was one of the major contributing factors to aerosols in Nainital. Using the EC tracer method, along with several approaches for the determination of the (OC/EC)pri ratio, the estimated SOC component accounted for ~25% (19.3–29.7%). Furthermore, TSP source apportionment via PMF allowed for a better understanding of the aerosol sources in the Central Himalayan region. The key aerosol sources over Nainital were BB (27%), secondary sulfate (20%), secondary nitrate (9%), mineral dust (34%), and long-range transported mixed marine aerosol (10%). The potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses were also used to identify the probable regional source areas of resolved aerosol sources. The main source regions for aerosols in Nainital were the plains in northwest India and Pakistan, polluted cities like Delhi, the Thar Desert, and the Arabian Sea area. The outcomes of the present study are expected to elucidate the atmospheric chemistry, emission source origins, and transport pathways of aerosols over the central Himalayan region. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Relationship between Lightning and Aerosol Optical Depth over the Uttarakhand Region in India: Thermodynamic Perspective
(Multidisciplinary Digital Publishing Institute (MDPI), 2022) Alok Sagar Gautam; Abhishek Joshi; Sagarika Chandra; Umesh Chandra Dumka; Devendraa Siingh; Ram Pal Singh
The current study is mainly focused on the monthly variation in the lightning flash rate (LFR) and related thermodynamic parameters using the data for the years 2000–2013, and the trend of lightning variation is explored. Lightning data are used from a lightning imaging sensor (LIS) and an optical transient detector (OTP) boarded on the tropical rainfall measuring mission (TRMM). Additionally, aerosol optical depth (AOD) data at 550 nm for the same period were considered from a Moderate Resolution Imaging Spectroradiometer (MODIS). The assessment of lightning and AOD using monthly data makes it difficult to study seasonal contributions, and higher-resolution (hourly) data may be more appropriate, but unfortunately, no data were available with a higher resolution than monthly. The dependency of LFR is also investigated using thermodynamic/dynamic parameters. The LFR shows a moderate correlation with a correlation coefficient of 0.56, 0.62, and 0.63 for AOD, CAPE, and vertical velocity, respectively. The increasing AOD in the pre-monsoon season is associated with higher lightning flash rates over this region. The possible sources of aerosols that cause an increase in lightning activities are identified from the classification of aerosols based on the characteristic values of the AOD and the Ångström exponent. The thermodynamic relation of the Product of Bowen ratio with the sum of the precipitation rate and evaporation rate has been used as a proxy to evaluate the lightning flash rate density over Srinagar, Uttarakhand region (78.55° E–79.05° E, 29.97° N–30.47° N), with nine models from the Coupled Model Inter-comparison Project-Phase 5 (CMIP5). The model-simulated LFR has also been used for the projection of lightning in the late 21st century, and the projected LFR over the study area shows a 7.41% increase during the (2079–2088) period as compared to the historic period (1996–2005). The results of the study region indicate caution in using any single climate variable as a proxy for projecting a change in the lightning–climate relationships in the scenario of global warming. © 2022 by the authors.
Spatio-temporal analysis of air pollution and meteorological influences in western Uttar Pradesh using Geospatial techniques: insights for policy and management
(Taylor and Francis Ltd., 2025) Ram Pravesh Kumar; Aafreen Jahan; Ranjit Singh; Pradeep Kumar; Rajesh Bag; R. Bhatla; Balram Ambade; Umesh Chandra Dumka
Recently, air pollution has emerged as a critical environmental challenge, posing significant risks to human health and ecosystems. This study presents a comprehensive spatiotemporal assessment of six major air pollutants (PM₂.₅, NO₂, SO₂, O₃, CO, NH₃) across seven cities of Western Uttar Pradesh (WUP), India (2019–2022), using Geospatial techniques. The findings reveal significant seasonal and spatial variability driven by anthropogenic emissions and meteorological factors. PM₂.₅ levels peaked during winter, ranging from 140 to 181 µg m−3 in Ghaziabad and NOIDA, exceeding the CPCB annual standard by over 9–13 times. NO₂ concentrations also peaked in winter, surpassing 80 µg m−3 in industrial areas, while SO₂ exhibited summer maxima exceeding 25 µg m−3 in Bulandshahr and Agra. O₃ levels were highest during summer and post-monsoon, increasing from 38.03 µg m−3 to 51.20 µg m−3 in Muzaffarnagar over the study period. CO concentrations remained high in winter, reaching 1.54 mg m−3 in NOIDA, and NH₃ showed post-monsoon peaks exceeding 35 µg m−3 in agricultural regions. Correlation analysis showed strong associations between PM₂.₅ and NO₂ (r = 0.80), and NH₃ (r = 0.67), indicating dominant emission sources from vehicular, industrial, and agricultural activities. Random forest regression identified temperature Relative Importance Scores (RIS 0.258) and relative humidity (RIS = 0.242) as key predictors for PM₂.₅, with the model explaining 69.1% of its variability (R2 = 0.691). Air Quality Index (AQI) analysis revealed that Ghaziabad and Baghpat experienced 60.64% and 40.86% of days in the ‘Severe’ category, respectively, highlighting critical air quality deterioration. These findings emphasize the urgent need for season-specific and location-sensitive air pollution mitigation strategies that integrate emission control and meteorological influences to improve public health and environmental sustainability in WUP, aligning with Sustainable Development Goals 3 and 11. © 2025 Informa UK Limited, trading as Taylor & Francis Group.
Understanding carbon sequestration trends using model and satellite data under different ecosystems in India
(Elsevier B.V., 2023) Smrati Gupta; Pramit Kumar Deb Burman; Yogesh K. Tiwari; Umesh Chandra Dumka; Nikul Kumari; Ankur Srivastava; Akhilesh S. Raghubanshi
This study discusses carbon sequestration variability in different ecosystems of India. Four different biosphere regions, each over 0.5° × 0.5° area, have been selected considering the geospatial and climatic variability of these regions expanding from Central India (CI), the Northeast region (NER), the Western Ghats (WG), and the Western Himalayan region (WHNI). The climatic conditions of these four regions are different so are the biosphere constituents of these regions. We expect the Gross Primary Productivity (GPP) to enhance during the all India summer monsoon rainfall season but in varied magnitudes suggesting a role of climatic parameters and flora in these regions. The GPP from FLUXCOM for the duration of 2001 to 2019 (19 years) and satellite-derived vegetation indices like the Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) are used in this study to understand the response of regional vegetation to this variability. EVI seems to be better related to GPP in comparison to NDVI in the preliminary analysis. Further analysis suggests LAI correlates better to GPP than EVI and NDVI in different seasons in these four regions. Also, meteorological parameters like surface temperature, rainfall, soil water, and other derived parameters like Vapor Pressure Deficit (VPD) are studied. It is also observed that the year-to-year variability in the climatic conditions could also have a role to play in the observed features. It is proven that the climate around the world is experiencing changes. Vegetation is one of the potent markers to monitor the impact of climate change. These long-term data and trends were studied to understand if there is any significant impact of the changing climatic conditions on the vegetation in these regions. Our study shows that there is an increasing (positive) trend in GPP at these locations though at different rates. WG and WHNI have shown a significant high rate of increase (6.44 and 5.36 gCm−2 y−1, respectively) in GPP over the last two decades. © 2023 Elsevier B.V.