Articles

• Quantify the changes in landscape patterns and their impact on ecosystem services values using land use land cover data in the middle reaches of the Damodar river basin

  1-15

  Sougata

  Druheen Chakrabortty Chakrabortty

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  7

  Human activities continuously modify the landscape area for their purpose which forces the landscape structure to change continuously. Therefore, it is essential to examine the impact of changing landscape structure on Ecosystem services values (ESV). The study has quantified the dynamic of ESV using land use land cover data and landscape metrics. The study has applied the Costanza et al. (1997 &2014) method to estimate ESV in the Middle reaches of the Damodar River Basin area and the Getis-Ord Gi* technique to delineate the dynamic hot spot and cold spot region in ESV within the stipulated period. The study has shown that ESV varies with the changes in landscape structure. The diminishing of vegetation, agricultural land, water body area and the expansion of built-up area has shifted the ESV zone from the North-West part in 2000-2012 to the wider part of North-West and North-East in 2012-2023 and 2000-2023 periods and marked the North-West and North-East part as a more dynamic zone within the study period.   

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• Exploring air quality and health effects during diwali: A comprehensive study in Lucknow city

  16-32

  Suleman

  Views:

  3

  Diwali, a revered cultural festival nationwide, poses significant environmental challenges, especially in urban hubs like Lucknow. Traditionally marked by elaborate rituals and widespread firecracker usage, Diwali contributes notably to air pollution. Amid mounting concerns over environmental degradation's health impacts, this study examines Diwali's air pollution dynamics, indoor exposure factors, and health effects in Lucknow. By dissecting these intricacies, it seeks to inform targeted interventions for environmental preservation and community health enhancement. The study adopts an exploratory survey design, employing both primary and secondary data collection methods. Primary data, acquired through structured questionnaires, involves 523 parents and 179 doctors in Lucknow, Uttar Pradesh. Quota and purposive sampling techniques were utilised to select participants meeting specific criteria. Statistical analyses, including frequency, Chi-square, and exploratory factor analysis, were conducted using SPSS version 25 to achieve research objectives. The results of the Diwali 2023 air pollution survey in Lucknow City indicate a significant rise in PM₁₀ and PM_2.5 levels, notably during nighttime, attributed to firecracker burning. Slight increases in SO₂ and NO₂ suggest additional fuel combustion. Further, hypotheses testing associations between locality types and indoor air pollution sources revealed significant links to factors like burning mosquito repellents, candles, and genetic predispositions. Doctors highlighted children in flats/apartments, aged 0-4, as most vulnerable, citing socioeconomic and behavioural influences. Seasonal variations and festivals, particularly Diwali, exacerbate air pollution effects. Doctors noted diverse health impacts, from respiratory issues to neurological effects, stressing the need for comprehensive mitigation efforts.

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• A deep analytics for prediction and forecasting of air quality in Chennai

  33-53

  S Sasikala

  R Shalini

  D Chinnapparaj

  Views:

  6

  Air pollution is a global crisis with profound implications for public health and environmental sustainability. In addressing this issue in Chennai, Tamil Nadu, a novel Hadoop-based real-time air pollution prediction system is proposed. This research offers accurate air quality information for specific Chennai regions, aiding decisions and mitigating pollution risks through big data analytics and deep learning for air quality prediction. To expedite air quality prediction, a vast air pollution dataset is rigorously analyzed using a Hadoop-based analytics model. Specific locations in Chennai, including Perungudi, Royapuram, Manali, Alandur, Arumbakkam, Kodungaiyur, and Velachery, are assessed for upto- date air quality evaluations. The core of the research revolves around implementing deep learning models—Long Short-Term Memory, Convolutional Neural Network, and a hybrid Long Short-Term Memory-Convolutional Neural Network model. These models are trained to forecast AQI for selected areas over the next five years, with the hybrid model emerging as the standout performer, achieving 99% of accuracy rate and mean absolute error, mean square error, root mean square error rates of 7.95, 101.71, 9.65. This high accuracy and low error rates empowers policymakers and environmental agencies to make informed decisions, fostering healthier living conditions in Chennai. The integration of big data analytics and deep learning, promises improved air quality management in urban areas globally, addressing similar environmental challenges and enhancing overall quality of life.

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• Prioritizing Watersheds for Flood Risk Assessment in Uttarakhand Himalayas using Geospatial Techniques and TOPSIS Method

  54-71

  Abhishek Kumar

  Pardeep Kumar

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  5

  Uttarakhand has a highly diverse topography, with snow-covered peaks, deep canyons, roaring streams, and dusty plains, all drained by various rivers of the Ganges system, India. The present study prioritizes watersheds in the Uttarakhand Himalayas for flood susceptibility using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, supported by GIS and remote sensing data. ALOS PALSAR Digital Elevation Model (DEM) with 12.5-meter resolution was utilized to map topographic features and to analyze 18 morphometric parameters of 28 watersheds. The TOPSIS method prioritized sub-watersheds using AHP criteria weights, which are classified into five priority levels ranging from very low to very high. The Sarju, Ram Ganga, and Song watersheds were identified as having the highest flood risk, placing them in the “Very High” priority class. These watersheds exhibited high drainage density (Dd), stream frequency (Fs), and bifurcation ratio (Rb), indicating a dense and complex drainage network prone to rapid runoff and increased flood potential. The watersheds such as Bandagarh, Parry, and Chandra Bhaga were placed in the “Very Low” priority class due to lower closeness coefficient (Cci) values, suggesting simpler drainage systems and reduced flood risk. The AUC (Area Under Curve) value of 0.789, indicates a good predictive accuracy for the TOPSIS model. The classification helps in pinpointing high-risk areas that require urgent flood management interventions.

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