The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

We have examined the land surface changes in the Lidder Valley of Kashmir in the context of socio-economy, climate and environmental aspect using geoscience technology. It was found that there are large-scale changes in agriculture, horticulture and built-up in the subject area. Records for horticulture, agriculture and built-up were generated from satellite data. A geodatabase of socio-economic data is examined and corelated with the land system changes. Furthermore, environmental driving factors like precipitation, temperature, snowfall and black carbon (BC) data were used to relate with the changes in LU/LC over the study area. This study reports decrease in agricultural area from 189.73 km2 in 1979 to 77.18 km2 in 2011 while the horticulture is increasing by 3 km2 annually from last three decades. With the increase of about 95% in horticulture, the concentration of BC has also increased. BC is showing a significant decadal increase of 10.5 mg/m2 which is mainly attributed to the biomass burning from horticulture waste. Rainfall, as well as snowfall over the study area, is decreasing by an amount of 2.95 and 15 mm per decade, respectively, and also their pattern is changing. These variations over the study area result in water scarcity forcing people to implement horticulture.

Food safety remains a critical global challenge with far-reaching implications for public health, economic stability, and social well-being. Contamination from chemical and microbiological hazards is a major cause of food losses, with an estimated 14% of food products lost before reaching consumers. This review examines the role of smart technologies particularly digital tools in addressing key challenges related to food safety. The focus is on innovations that enhance detection, monitoring, and traceability across the food supply chain. Integrating these advanced technologies enables early contaminant detection, accurate risk prediction, and improved management of food safety practices throughout production, distribution, and consumption. The adoption of smart technologies is essential to strengthening gl@@obal food safety systems, reducing foodborne illnesses, and ensuring safer, more reliable food for consumers worldwide. Graphical abstract

Urban air pollution, particularly nitrogen dioxide (NO ₂ ), remains a critical environmental and public health concern in rapidly growing cities. This study explores the spatiotemporal patterns of NO ₂ concentrations in Chennai and Bengaluru from 2019 to 2023 by integrating satellite‐based datasets and statistical modeling on the Google Earth Engine (GEE) platform. Sentinel‐5P TROPOMI data were used to assess NO ₂ levels, Sentinel‐2‐derived NDVI represented vegetation cover, and Landsat 8 imagery provided land surface temperature (LST) estimates. Seasonal trends were analyzed for both summer (March–June) and winter (November–February) periods. Results revealed pronounced seasonal variability, with Chennai exhibiting consistently higher NO ₂ concentrations in winter, while Bengaluru displayed more stable or decreasing trends. Notably, NO ₂ levels in Chennai rose by 15.4% during summers over the study period, whereas Bengaluru saw a 16.6% decrease. A comparative regression analysis showed that the relationship between NO ₂ and vegetation cover (NDVI) strengthened in Chennai during winter ( R ² = 0.043 in 2023), suggesting reduced green cover may intensify pollutant accumulation. Conversely, Bengaluru showed stronger NO ₂ –NDVI correlations during summer ( R ² = 0.049 in 2023), indicating vegetation's role in pollutant mitigation during active growing seasons. The NO ₂ –LST relationship also varied: Chennai experienced the strongest positive correlation in summer 2022 ( R ² = 0.30), whereas Bengaluru exhibited increasing winter correlations, potentially driven by surface warming and enhanced atmospheric mixing. Although direct meteorological parameters such as rainfall, humidity, wind speed, solar radiation, and visibility were not included in the present analysis, their influence on NO ₂ dynamics is acknowledged and warrants future exploration. Overall, the findings underscore the complex, season‐specific interactions among urban heat, vegetation, and air pollution in different metropolitan contexts. These insights support the need for tailored, climate‐responsive pollution control strategies that integrate urban greening, emission reductions, and adaptive planning.