A New Environment Air Quality Evaluation System Based on Backpropagation Artificial Neural Network

Air is an essential stuff of life for every alive creature. The World Health Organization states that 2.4 million people die every year of causes directly addressed to air pollution. So, monitoring, evaluating and controlling systems of air quality have become important issues in recent years. An environment air quality evaluation system based on back propagation artificial neural network is proposed in this paper. To do so, all examinations have been executed by a designed simulator software. Carbon monoxide, nitrogen dioxide, sulfur dioxide, particular matters, oxygen, ozone and lead are basic parameters of the evaluation system. The environment air quality is classified in three levels, so that the Red LED in the designed system will turn on when the concentration of noxious gas is beyond the standard condition. Yellow LED will turn on when the input parameters are in a tolerable condition. Green LED will turn on when the concentration of noxious gas and other parameters in air are in a standard condition. Standard levels are derived from National Ambient Air Quality Standards (NAAQS) and considered as harmful consequences on public health and the environment. The proposed system is based on using artificial neural network for air quality classification. The results demonstrate that this network is feasible and effective in improvement of air quality evaluation systems.

Keywords:

Air evaluation system , artificial neural network , Back Propagation algorithm , sensors