Abstract
The dispersions or suspensions of the particles in solid and the liquid forms in atmosphere are coined as aerosols. These suspensions are the matter of concern in recent times as the ecosystem and the human health are at risk. These atmospheric aerosols are defined in broader terms; to be more precise, the term particulate matter (PM) is used to define the suspended solid-phase matter in the atmosphere. It is the mixture of the diverse elements. Further pollutants like SO2 and NO2 are largely found in the industrial waste. The evidences reveal that sulfate and organic matter are the two main contributing factors for annual PM10 concentration, and its consequences are like health problems and ecological imbalance which are correlating and pointing especially toward the particulate matter. In this paper, the average concentration of various pollutants like SO2, NO2, PM10, and SPM in air have been predicted efficiently. The detailed analysis of different models and its effects on the environment have been examined with the help of neural network tool.
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Khokhar, J.S. et al. (2019). A Neural Network-Based Novel Detection Technique for Analysis of Pollutant Levels in Air. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 669. Springer, Singapore. https://doi.org/10.1007/978-981-10-8968-8_1
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DOI: https://doi.org/10.1007/978-981-10-8968-8_1
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