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• Chemical and biological air pollutants, as parameters of complex air quality indices

  16-23

  Tekal Eötvös

  László Makra

  Views:

  101

  Human health is essentially influenced by air quality. Atmospheric air in residential areas contains many pollutants. The monitoring and the plain publishing of the measured values are important both for the authorities and the public. Air quality is often characterized by constructing air quality indices, and these indices are used to inform the public. The construction of an advanced air quality index is usually done by averaging the measured data usually in time and space; hereby important aspects of the data can be lost. All known indices contain only chemical pollutants, while certain biological pollutants can enhance the effects of the chemical pollutants and vice versa. In this paper we discuss the importance of integrating biological pollutants into air quality indices. In order to increase efficacy of these indices to the civil society we aim to introduce geographic information system (GIS) methods into publishing air quality information.

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

  33-53

  S Sasikala

  R Shalini

  D Chinnapparaj

  Views:

  43

  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.