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• Environmental educational potentials on school grounds in Budapest

  23-30

  Eszter Jákli

  Views:

  288

  As nature and greenspaces in urban areas are agreed to enhance children’s appreciation towards nature and so the purposes of environmental education, it is of high importance to create spaces in and around schools that allow students to connect to nature on a daily basis. The aim of the study was to analyse functions and other components supporting environmental education appear in the open spaces of school grounds in Budapest, and to understand the main characteristics of school grounds with the highest potential in environmental education. The study points out that the presence of environmental educational functions often depends on the size, urban context and location of the school grounds, however the curriculum of the school does not necessarily influence its open spaces, while the presence of motivated and engaged teachers does. The study reveals environmental educational functions do exist in school grounds of primary schools in Budapest, however they play only secondary role behind active movement and play functions. The schools with the best potentials in environmental education are without doubt the ones situated on large plots in the suburban zone, mostly with a high proportion of green spaces in and around the school grounds.

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

  33-53

  S Sasikala

  R Shalini

  D Chinnapparaj

  Views:

  45

  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.