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Urban Heat Dynamics in Pune: The Influence of Land Cover and Local Climate

Urban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (Tmax; 0.13? year?1 to 1.63? year?1) at almost each study site and minimum temperature (Tmin; 0.06? year?1 to 0.23? year?1) at a specific location during night. The mutual effect of land cover changes and meteorological conditions have evidenced the heat islands with varying intensities (2? to 8?) at four of the six sites, with significantly intensifying rates from 0.05? year?1 to 0.39? year?1. The estimation of dominating land cover type for the formation of heat islands demonstrated a significant simple determination (r2 = 0.001 to 0.013) and probability (P < 7.910?13 to 2.330?5) with heat island temperature identifying urban land cover as the primary factor at two sites, while the other two were affected by mixed land covers influenced by local meteorological characteristics. The outcomes of this study offer valuable insights into the development of heat islands in Pune and could guide strategies for alleviating urban heat, ultimately improving climate resilience and thermal comfort citywide. 2025, Binghamton University Libraries. All rights reserved.

Article

Multi-dimensional changes in drought patterns across India

Indias hydroclimatic systems are undergoing unprecedented transitions in a warming climate, marked by shifts in temperature extremes, altered precipitation patterns, and increasing drought risk. This study presents a comprehensive assessment of drought trends and hydroclimatic variability across six major geographical zonesWestern, Central, Himalayan, Indo-Gangetic Plain (IGP), Peninsular, and Northeast Indiaduring the period 1971 to 2020. Using a set of advanced climate change metricsStandardized Local Anomalies (SLA), Novel Climate Scores (NCS), and changes in probability of local climate extremes alongside the Standardized Precipitation Evapotranspiration Index (SPEI), we quantify changes in drought conditions and the emergence of non-analogue climates. Changes in climatic extreme are computed using high-resolution daily gridded temperature and rainfall datasets, comparing recent decades against a 19511980 baseline. SLA quantifies deviations from historical variability, highlighting intensified warming over the Indo-Gangetic Plain, western India, and the southern peninsula. NCS reveales the emergence of novel climatescombinations of temperature and precipitation conditions not previously observed, particularly in Southeast India and the Himalayan region. The probability of local climate extremes shows a substantial increase in extreme events across India indicating enhanced climate volatility. These metrics are then integrated with drought analysis using SPEI to incorporate both precipitation and temperature-driven evaporative demand. SPEI trends indicate increasing dryness in Northeast India, the Himalayas, and the Indo-Gangetic Plain, linked to declining monsoonal rainfall and rising temperatures. Meanwhile, Western and Peninsular regions show wetting trends, driven by increased rainfall and convective precipitation events. The rainfall is the dominant drought driver during the monsoon, while high maximum temperatures intensify drought conditions in pre- and post-monsoon seasons by enhancing evaporative demand. Minimum temperature exhibits regional effects, showing a drying influence in the IGP and Himalayas, but a slight moistening signal in Peninsular India. By combining drought indices with climatic extremes metrics, this study offers a comprehensive framework to monitor hydroclimatic shifts and their regional impacts. The findings underscore the need for region-specific adaptation strategies that incorporate early warning systems, sustainable water management, and climate-resilient agriculture to address Indias evolving drought risks. The Author(s), under exclusive licence to Springer Nature B.V. 2025.

Book Chapter

Harnessing the Transformative Potential of Blockchain Technology to Accelerate the Global Transition Toward a Scalable, Transparent, and Resilient Circular Economy Framework

Blockchain technology applied to circular economy has a world-changing potential to increase transparency, traceability, and the sustainability of the resources management. With the shift towards regenerative industries, it is of the essence to have the capacity to track the lifecycle of used products, used materials, and waste securely. A new method (Circular Blockchain-Resource Lifecycle Management (CB-RLM) algorithm) is proposed in this work that is supposed to maximize the monitoring and incentivization resource flow in the context of decentralized systems. Using a blockchain as a platform allows conducting real-time monitoring of materials backtracking, and the proposed approach will provide a way of materials following all stages of production, consumption, reuse, and recycling. The algorithm combines the information presented by the means of the IoT-enabled devices to guarantee permanent verification of product statuses and ownership, whereas smart contracts automatically sanction the policy compliance of reuse and returns. The system also creates digital tokens as rewards to sustainable behavior during processes, which gives the stakeholders incentive to engage in the circular economy. The mechanism will not only facilitate resource efficiency and accountability but it will promote trust among the members of the supply chain. The suggested CB-RLM algorithm also has the potential to solve such key design problems as data integrity, stakeholder cooperation and lifecycle openness, thus paving the way to the creation of scalable and smart circular systems. This model brings a more environmentally responsible and resilient industrial ecosystem by means of decentralized enforcement and automated incentives. The suggested CB-RLM technique achieves an overall accuracy of 99.4%. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Conference Paper

Exploratory Analysis and Pattern Recognition in Energy Production and Demand: A Data-Driven Approach Using Multi-Source Energy Metrics

Hydropower is a leading renewable energy source due to its high efficiency and low operational costs. However, it still faces significant environmental, operational, and forecasting challenges. This paper explores the use of machine learning (ML) models such as SARIMA, Random Forest (RF), and Neural Basis Expansion Analysis for Time Series (NBEATS) to optimize hydropower operations. By analyzing diverse data sets, including hydrometeorological data, plant operations, sensor inputs, and other energy production and demand metrics such as solar, wind, coal, nuclear, and storage, ML enhances decision-making in areas such as inflow forecasting, predictive maintenance, and environmental sustainability. The paper presents an exploratory analysis of 48 -hour energy production and demand patterns across multiple sources (Hydro, Coal, Solar, Wind, Nuclear, and Storage), offering insights into interdependencies and system behavior. It also reviews current ML applications in hydropower, highlights challenges such as data quality and model interpretability, and discusses emerging technologies such as reinforcement learning, explainable AI (XAI), and digital twins as promising future directions. 2025 IEEE.

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