Vinay Kumar

The growing demand for energy and emerging environmental concerns are making it necessary to look for more sustainable alternatives. To address the limitations of first-generation biofuels and reduce dependence on fossil fuels, this study focuses on second-generation bioethanol sourced from non-edible pomegranate waste. This study develops and analyses a supply chain optimization model for the sustainable production of biofuel from pomegranate waste and solves it using a genetic algorithm. The framework assesses key supply chain elements, including collection centres for pomegranate waste, processing plants, bio-refineries for conversion and distribution centres for final bioethanol. The primary objective of the optimization is to reduce the total cost of the biofuel production system and to maximize positive environmental impact through waste valorization. A numerical example validates the framework, and a sensitivity analysis further evaluates the economic viability of the supply chain under fluctuating market conditions, such as variations in the purchasing cost of waste, the production cost of bioethanol and the opening cost of plants. Biofuel production supports the Sustainable Development Goals (SDG-12 and -13) by transforming waste into renewable energy. This study aims to address gaps in biofuel research by focusing on the underutilized area of pomegranate-based biofuel through an integrated supply chain optimization framework. The findings offer practical values for researchers working on renewable energy solutions, policymakers and business leaders.

Persistent droughts and shifting precipitation regimes continue to threaten water security across the United States, with arid and semi-arid regions remaining the most vulnerable. This study examines the spatial and temporal patterns of aridity and water scarcity across drought-prone stations (111) and regions of the U.S. using 30 years (1991–2020) of precipitation records from xmACIS II. Weather stations were categorized into arid (20 inches/year) zones, revealing a distinct west–east gradient: arid and semi-arid conditions prevail across the western and central U.S., while the eastern regions remain largely non-arid. Drought frequency analysis spanning 2000–2019 indicates that certain regions experienced exceptional drought conditions (D3 or higher) for more than 50% of the study period, with localized areas enduring over 300 weeks of extreme drought. Long-term precipitation trends (1920–2020) in Texas, Washington, and South Dakota reflect a modest increase in precipitation; however, CMIP6 multi-model ensemble projections under a 2 °C and 4 °C warming scenario point to divergent future trajectories, with some regions experiencing increased wetness while others face progressive drying. These findings offer actionable insights for drought monitoring and climate adaptation strategies, underscoring the heightened vulnerability of arid and semi-arid zones to intensify water scarcity.

Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR datasets are often utilized to fill observational gaps. However, these datasets are subject to systematic biases, particularly under diverse sky and seasonal conditions. This study presents a comprehensive evaluation of four widely used SSR datasets: ERA5, IMDAA, MERRA2, and CERES, against high-quality in situ observations from 27 India Meteorological Department (IMD) stations, for the period 1985–2020. The assessment incorporates multi-scale temporal analysis (daily/monthly), spatial validation, and sky-condition stratification via the clearness index (K_t). The results indicate that CERES exhibits the best overall performance with the lowest RMSE (16.30 W/m²), minimal bias (–2.5%), and strong correlation (r = 0.97; p = 0.01), particularly under partly cloudy conditions. ERA5, with a finer spatial resolution, also performs robustly (RMSE = 20.80 W/m²; MBE = –0.8%; r = 0.94; p = 0.01), showing consistent agreement with observed seasonal cycles, though slightly underestimating SSR during monsoonal cloud cover. MERRA2 shows moderate overestimation (+4.4%) with region-specific bias variability, while IMDAA demonstrates persistent overestimation (+10.2%) across all conditions, highlighting limited sensitivity to atmospheric transparency. Importantly, this study reconciles apparent contradictions between monthly and sky condition-based bias analyses, attributing them to aggregation differences. While reanalysis datasets overestimate SSR during the monsoon on average, they tend to underestimate it under heavily overcast conditions. These insights are critical for guiding the selection and application of SSR datasets in solar energy modelling, SPV system design, and climate diagnostics across India’s heterogeneous atmospheric regimes.

The evaluation of cloud distribution, properties, and their interaction with the radiation (longwave and shortwave) is of utmost importance for the proper assessment of future climate. Therefore, this study focuses on the Coupled Model Inter-Comparison Project Phase-6 (CMIP6) historical and future projections using the Shared Socio-Economic Pathways (SSPs) low- (ssp1–2.6), moderate- (ssp2–4.5), and high-emission (ssp5–8.5) scenarios along the South Asian region. For this purpose, a multi-model ensemble mean approach is employed to analyze the future projections in the low-, mid-, and high-emission scenarios. The cloud water content and cloud ice content in the CMIP6 models show an increase in upper and lower troposphere simultaneously in future projections as compared to ERA5 and historical projections. The longwave and shortwave cloud radiative effects at the top of the atmosphere are examined, as they offer a global perspective on radiation changes that influence atmospheric circulation and climate variability. The longwave cloud radiative effect (44.14 W/m²) and the shortwave cloud radiative effect (−73.43 W/m²) likely indicate an increase in cloud albedo. Similarly, there is an expansion of Hadley circulation (intensified subsidence) towards poleward, indicating the shifting of subtropical high-pressure zones, which can influence regional monsoon dynamics and cloud distributions. The impact of future projections on the tropospheric temperature (200–600 hPa) is studied, which seems to become more concentrated along the Tibetan Plateau in the moderate- and high-emission scenarios. This increase in the tropospheric temperature at 200–600 hPa reduces atmospheric stability, allowing stronger convection. Hence, the strengthening of convective activities may be favorable in future climate conditions. Thus, the correct representation of the model physics, cloud-radiative feedback, and the large-scale circulation that drives the Indian Summer Monsoon (ISM) is of critical importance in Coupled General Circulation Models (GCMs).

Sri Lanka typically experiences anomalously wet conditions during the summer following El Niño events, but this response varies due to El Niño complexity. This study investigates the impact of post-El Niño conditions on Sri Lanka’s Monsoon rainfall, contrasting summers after fast- and slow-decaying El Niño events. Results indicate that fast-decaying El Niño events lead to wet and cool summers while slow-decaying events result in dry and warm summers. These contrasting responses are linked to sea surface temperature (SST) changes in the central to eastern Pacific. During the fast-decaying El Niño, the transition to La Niña generates strong easterlies in the central and eastern Pacific, enhancing moisture convergence, upward motion, and cloud cover, resulting in wetter conditions over Sri Lanka. During the fast-decaying El Niño, enhanced precipitation over the Maritime Continent acts as a diabatic heating source, inducing Gill-type easterly wind anomalies over the tropical Pacific. These winds promote coupled feedbacks that accelerate the transition to La Niña, strengthening moisture convergence and upward motion over Sri Lanka. Conversely, slow-decaying El Niño events are associated with cooling in the western North Pacific and warming in the Indian Ocean, which promotes the development of the western North Pacific anticyclone, suppressing upward motion and reducing cloud cover, leading to conditions over Sri Lanka. Changes in the Walker circulation further contribute to these distinct rainfall patterns, highlighting its influence on regional climate dynamics. These findings enhance our understanding of the seasonal predictability of rainfall in Sri Lanka during post-El Niño Summers.

This study evaluated baseline groundwater quality near the proposed Direct Air Capture (DAC) facility in Kingsville, TX, USA. It hypothesized that groundwater quality in the region has remained relatively stable over time. Rainwater and groundwater data from NOAA, along with records from approximately 30 water wells, were analyzed. A detailed assessment was conducted on Well ID 8329702, which had the most extensive and continuous dataset for alkalinity, bicarbonate, pH, and hardness from 1973 to 2012. The analysis revealed a slight decline in alkalinity (−0.00006) and bicarbonate (−0.007) over time, while changes in pH and hardness were minimal. These findings provide a critical baseline for future groundwater monitoring, particularly in areas surrounding industrial facilities such as DAC plants.

Arabian Sea (AS) and Bay of Bengal (BoB) cyclones around the Indian subcontinent cause widespread floods and other natural hazards. There is no single convincing answer to this puzzle in the era of global warming. The warming of the western and central Indian Ocean is one of the few prominent features of local warming. The availability of moisture in the atmosphere in the last decade is an important factor in the rapid intensification and strengthening of tropical cyclones (TCs) before landfall. Essentially, the AS basin has shown an upward trend in the number and intensity of very severe cyclones during the period of 2009–2019. The decadal variation (1991–2001, 2002–2011, and 2012–2021) in SST, vorticity, wind shear, and moisture is primarily responsible for the genesis and intensification of cyclones during the post-monsoon season (October–November–December) over the AS. The results showed that slight changes in wind conditions, such as increased wind shear and the northward shift of the Asian Jet Stream over the region, facilitate TC formation.

Projecting future changes in monsoon rainfall is crucial for effective water resource management, food security, and livestock sustainability in South Asia. This study assesses precipitation, total cloud cover (categorized by cloud top pressure), and outgoing longwave radiation (OLR) across the region using Coupled Model Intercomparison Project Phase 6 (CMIP6) data. A multi-model ensemble (MME) approach is employed to analyze future projections under the Shared Socio-Economic Pathway (SSP5-8.5) scenario, which assumes radiative forcing will reach 8.5 W/m² by 2100. The MME projects a ~1.5 mm/day increase in total rainfall during 2081–2100. Convective and stratiform precipitation are expected to expand spatially, with convective rainfall increasing from 3 mm/day in historical simulations to 3.302 mm/day in the far future. Stratiform precipitation also shows an increase from 0.822 mm/day to 0.962 mm/day over the same period. A notable decrease in OLR (~60 W/m² along the Western Ghats) and an increase in high cloud cover suggest intensified monsoon rainfall. The pattern correlation coefficient (PCC) reveals reduced OLR in future scenarios (PCC ~0.77 vs. ~0.81 historically), likely due to cloud feedback mechanisms. These results highlight enhanced monsoonal activity under warming scenarios, with implications for regional climate adaptation.

Forests are vital for life on Earth but are threatened by forest fires, which have significant impacts on climate change both locally and globally. This study examines a forest fire that lasted from 15 to 26 February 2019 in Karnataka, India, using the Weather Research and Forecasting model with Chemistry (WRF-Chem) model to analyze the effects and atmospheric spread of fire-emitted aerosols. Model simulations are analyzed to understand the horizontal and vertical transport and radiative effects of the fire. The results show high aerosol levels and smoke particles reaching up to 3.5 km altitude and above. The fire raised near-surface air temperatures by ~1–1.5 °C. The net atmospheric forcing due to the fire over the affected area ranged from approximately 10 to 14 W/m², resulting in heating rates between about 0.002 and 0.005 K/day in the impacted region.

The formation of low-pressure systems (LPSs) over the eastern Atlantic Ocean, near the coast of West Africa, is an exceptional meteorological/climatological feature that can lead to the development of hurricanes. The upper level diffluence induced by the Tropical Easterly Jet (TEJ) plays a crucial role in the formation of LPSs over the eastern Atlantic Ocean, off the coast of West Africa. However, the exact influence of the enhanced TEJ and diffluence in relation to cyclogenesis remains unclear. An active precipitation period over the Indian subcontinent and Africa induces an intensification of the TEJ, African Easterly Jet, and the bifurcation of diffluence off the coast of Africa. Over the past five years (2019–2023), a delayed correlation has been observed between the formation of LPSs over the eastern Atlantic Ocean (7.5° N–20° N, 15° W–41° W), the TEJ over the Indian subcontinent (approximately 2 to 3 days), and the AEJ over Africa (approximately 1 day). This correlation is further linked to the bifurcation of diffluence at the 200 mb level.