Browsing by Author "Arti K. Choudhary"

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Assessing the accuracy of different Z-R relationships for Doppler Weather Radar based rainfall estimation: A comparative study for the Delhi region
(Elsevier Ltd, 2025) J. Sharma; Arpita Rastogi; Shikha Verma; Gajendra Kumar; Arti K. Choudhary
Z-R relationships are the most used methods for calculating rainfall using radar reflectivity, which provide a relation between the radar reflectivity (Z) to rainfall rate (R). However, different Z-R relationships may yield varying rainfall estimates depending on regional climatic conditions and radar characteristics. This study presents a comparison of various Z-R relationships (Marshall-Palmer (Z = 200R1.6), WSR-88D (Z = 300R1.4), and Rosenfeld tropical (Z = 250R1.2)) for the Delhi radar station for 2019. The study was performed for four seasons (Winter, Pre-Monsoon, Monsoon, and Post-monsoon) as well as for different rainfall intensity (Light, Moderate, and Heavy rain). The accuracy of each relationship was evaluated using statistical variables such as correlation coefficient (R), RMSE, MAE, Bias and NSE. Results indicate significant variability in rainfall estimation across different relationships. The Marshall-Palmer encompasses the best correlation with rain gauge data during the monsoon and post-monsoon, whereas the Rosenfeld Tropical exhibits the strongest correlation for the winter and pre-monsoon. Additionally, Rosenfeld Tropical has a strong correlation for moderate and heavy rainfall intensity, whereas Marshall Palmer offers a satisfactory correlation for light rainfall intensity. However, Marshall-Palmer offers the best performance for the overall dataset with R = 0.623, RMSE of 13.44, and MAE = 10.07, as well as the lowest RMSE for all seasons and rainfall intensity. These findings highlight the significance of selecting a correct Z-R relationship for accurate rainfall estimation in diverse meteorological conditions, and underscore the need for localized calibration of Z-R parameters for enhanced forecasting accuracy in the Delhi region. © 2025 Elsevier Ltd.
Machine learning models for estimating criteria pollutants and health risk-based air quality indices over eastern coast coal mine complex belts
(Frontiers Media SA, 2025) Pradeep Kumar; Arti K. Choudhary; Pawan Kumar Joshi; Ram Pravesh Kumar; R. Bhatla
Estimating criteria pollutants is crucial due to their continuous increase and impact on respiratory health. To mitigate the impact of air pollution on human health, it is essential to understand the concentration of air pollutants at specific locations. This study aims to evaluate the variation, estimate the levels of criteria pollutants, and assess their potential health risks in the vicinity of a coal mine complex and a thermal power plant situated in an eastern coastal state of India. The pre-existing hot spot regions—Talcher (T) and Brajrajnagar (B)—which host many coal-fired power plants and clusters of coal-mining blocks in the coastal state of Odisha, are considered. Talcher consistently shows higher levels of particulate matter (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2), reflecting a greater industrial impact. Brajrajnagar, while also impacted, exhibits comparatively lower pollutant concentrations. The observed seasonal trends highlight the necessity for targeted mitigation strategies to address pollution levels and associated health risks in these regions. Novel machine learning (ML) models, including independent component regression (ICR), ElasticNet (ENET), and boosted tree (BT), are applied to estimate criteria pollutants. Statistical analyses highlight BT as the superior model, outperforming ENET and ICR in pollutant estimation, particularly in Talcher. Taylor plots and statistical evaluations further validate the BT model’s robustness in air pollutant estimation. Additionally, the study assesses the associated health risks posed to nearby populations of Talcher and Brajrajnagar. The analysis highlights significant spatial disparities in pollution levels, with Talcher consistently recording higher concentrations of PM10, NO2, and SO2 and poorer air quality index (AQI) than Brajrajnagar. Talcher also shows greater health risks, with pollutant exposure linked up to 6% higher risks for PM10, 5% for NO2, and up to 3% for SO2. The health risk-based air quality index (HAQI) reveals an underestimation of health risks by the current AQI, emphasizing the need for improved metrics to address the impacts of multi-pollutant exposure. © © 2025 Kumar, Choudhary, Joshi, Kumar and Bhatla.
Physical, optical and radiative attributes of atmospheric aerosols produced due to bonfire during the Holika festival
(Elsevier Ltd, 2025) Bharat Ji Mehrotra; Arti K. Choudhary; Atul Kumar Kumar; Sudhir Kumar Sharma; Manoj Kumar Srivastava
Air pollution is a global health issue, and events like forest fires, agricultural burning, dust storms, and fireworks can significantly worsen it. Festivals involving fireworks and wood-log fires, such as Diwali and Holi, are key examples of events that impact local air quality. During Holi, the ritual of Holika involves burning of biomass that releases large amounts of aerosols and other pollutants. To assess the impact of Holika burning, observations were conducted from March 5th to March 18th, 2017. On March 12th, 2017, around 1.8 million kg of wood and biomass were openly burned in about 2250 units of Holika, located in and around the Varanasi city (25.23 N, 82.97 E, ∼82.20 m amsl). As the Holika burning event began the impact on the Black Carbon (BC), particulate matter 10 & 2.5 (PM10 and PM2.5), sulphur dioxide (SO2), oxides of nitrogen (NOx), ozone (O3) and carbon monoxide (CO) concentration were observed. Thorough optical investigations have been conducted to better comprehend the radiative effects of aerosols produced due to Holika burning on the environment. The measured AOD at 500 nm values were 0.315 ± 0.072, 0.392, and 0.329 ± 0.037, while the BC mass was 7.09 ± 1.78, 9.95, and 7.18 ± 0.27 μg/m3 for the pre-Holika, Holika, and post-Holika periods. Aerosol radiative forcing at the top of the atmosphere (ARF-TOA), at the surface (ARF-SUR), and in the atmosphere (ARF-ATM) are 2.46 ± 4.15, −40.22 ± 2.35, and 42.68 ± 4.12 W/m2 for pre-Holika, 6.34, −53.45, and 59.80 W/m2 for Holika, and 5.50 ± 0.97, −47.11 ± 5.20, and 52.61 ± 6.17 W/m2 for post-Holika burning. These intense observation and analysis revealed that Holika burning adversely impacts AQI, BC concentration and effects climate in terms of ARF and heating rate. © 2025 Elsevier Ltd
Quantification and spatial assessment of industrial Cd and Pb emission across India
(Taylor and Francis Ltd., 2025) Madhusmita Mishra; Ashirbad Mishra; Poonam Mangaraj; Atul Kumar Kumar; Gufran Beig; Pallavi Sahoo; Sridhara Nayak; B. Anjan Kumar Prusty; Arti K. Choudhary; Saroj Kumar Sahu
Heavy metals, particularly cadmium (Cd) and lead (Pb), pose a significant environmental challenge worldwide owing to their detrimental effects on ecosystem sustainability. India, the most populous country in the world, presently faces severe contamination by heavy metals. This study identifies and quantifies the Cd and Pb emissions from the principal industrial sources at the district level across India, using the IPCC bottom-up approach for 2019. The developed emission inventory includes various industries, notably coal-based power plants, captive power plants, cement production, iron and steel manufacturing, non-ferrous metal production, municipal and biomedical waste incineration, the glass industry, and fly ash generated. Annual emissions were reported to be approximately 2,016 tonnes/year (t/yr) for Cd and 19,258 t/yr for Pb, where coal combustion across different industries emitted approximately 93 t of Cd and 927 t of Pb, with the energy sector contributing about 66% and fly ash accounting for over 80% of total emissions. Among non-ferrous metals, copper production is solely responsible for 44 t and 77 t of Cd and Pb, respectively. The research also identifies regional hotspots for Cd and Pb emissions across India, highlighting areas where targeted remediation strategies can support sustainable environmental management. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Spatiotemporal trends in sunshine hours over India during three decades from 1988 to 2018
(Nature Research, 2025) Arti K. Choudhary; Bharat Ji Mehrotra; Atul Kumar Kumar; Pradeep Kumar; V. K. Soni; Manoj Kumar Srivastava
This study delves the trends of sunshine reaching the earth surface, both temporally and spatially, across nine geographically diverse regions including 20 stations in India, spanning the years 1988 to 2018. The monthly sunshine hours (SSH) analysis concluded significant increment from October to May followed by significant drops from June to July in six regions, except northern inland and Himalayan region that showed comparatively opposite monthly trends. The trend analysis depicted annual negative trend in all geographical regions with different rate (east coast: − 4.88 h/year; west coast: − 8.62 h/year; northern inland − 13.15 h/year; central inland: − 4.71 h/year; Deccan plateau: − 3.05 h/year; north eastern region: − 1.33 h/year; Himalayan region: − 9.47 h/year; island location Arabian Sea: − 5.72 h/year and Bay of Bengal − 6.10 h/year). Seasonal trends were found significant decline, but due to regional meteorological variation, accompanying Twomey effect may lead levelling off in SSH over north east region during monsoon and post-monsoon season. Analogous to annual SSH trend, intra-annual anomaly results were also depicting consistent decline in all geographical locations of India. The study reveals persistent decline of SSH in Indian subcontinent on all temporal scales excluding north eastern region where mild seasonal levelling off was found. © The Author(s) 2025.