International Journal of Climate Conditions

Air Quality Index (AQI) is a composite indicator used to communicate ambient air-quality conditions and their potential health implications. This study examines spatial and seasonal variation in AQI between Gaya city and Kusdihra village, representing urban and rural settings in Gaya district, Bihar. Secondary AQI observations were obtained from the Central Pollution Control Board (CPCB) National Air Quality Index portal at eight fixed time-slots per day during two 20-day observation windows: 1–20 May 2024 (pre-monsoon) and 1–20 November 2024 (post-monsoon).

Wetlands are critical habitats for resident and migratory birds and are sensitive to changes in water availability, habitat condition and climatic variability, making avifaunal responses a valuable proxy for assessing overall wetland health. This study used long-term (2013–2020) avifaunal diversity and abundance as indicators of wetland health across four ecologically contrasting Tamil Nadu wetlands which are Pallikaranai Marshland (urban), Vedanthangal Bird Sanctuary (freshwater breeding colony), Karaivetti Bird Sanctuary (inland freshwater) and Point Calimere Wildlife and Bird Sanctuary (coastal) in relation to temperature and rainfall. All four sites showed positive long-term trends, but abundance patterns and climatic sensitivity were markedly site-specific: Vedanthangal sustained consistently high counts, Point Calimere showed pronounced migration-driven inter-annual fluctuation, Karaivetti increased gradually, and Pallikaranai remained persistently low.

One of the most important worldwide issues of the twenty-first century is climate change, which poses a threat to economies, ecosystems, and human welfare.Although governments and environmental organizations continue to implement mitigation and adaptation strategies, public engagement remains uneven, particularly in developing countries where competing socio-economic priorities often limit environmental action. Sport is increasingly recognized as a powerful social institution capable of influencing attitudes, behaviours, and community mobilization.

The convergence of agentic artificial intelligence, large language model (LLM) orchestration, and quantum annealing presents a transformative paradigm for climate policy computation. This paper introduces Nega Optima a multi-agent computational architecture that integrates LLM-driven policy parsing, quantum annealing-based combinatorial optimization, and real-time integrated assessment model (IAM) feedback loops to simulate negative emission technology (NET) deployment strategies in a post-1.5°C overshoot scenario. Existing carbon capture optimization frameworks suffer from three systemic limitations: static policy inputs, classical solver intractability at continental deployment scales, and the absence of closed-loop policy refinement mechanisms.

We are moving from a reactive relationship with the weather to a symbiotic one. This AI-IoT framework is more than just a technological upgrade; it is a vital adaptation strategy in an age of climate volatility. As climate change induces unprecedented volatility in global weather patterns, traditional meteorological forecasting models often struggle to maintain hyper-local accuracy. This paper explores the convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) to create an autonomous, real-time rainfall prediction framework.

India is currently experiencing a significant transformation in its climate system, characterized by rising temperatures, irregular rainfall patterns, and an increasing frequency of extreme weather events. These changes are no longer gradual or isolated but are becoming more visible and impactful across different regions of the country. This study provides a detailed examination of long-term climatic trends in India by analysing temperature variations, monsoon behaviour, and the growing occurrence of extreme events such as heatwaves, floods, and cyclones, with a focus on recent developments up to 2025–2026.