Air pollution monitoring system based on wireless sensor networks

Air pollution is the biggest environmental hazard that cannot be ignored. Due to increase in number of industries and urbanization increases air pollutants concentrations in many areas because of this different changes are been happening in human life like health issues and as well as other living organisms. We have some pollutant emission monitoring systems, like Opsis, Codel, Urac and TAS-Air metrics which are expensive. As well as these systems have limitations to be installed on chimney due to their principle of operation. In this work I like to propose a function that is easy to use and causes less cost compared to the other ones. That is an industrial air pollution monitoring system based on the technology of Wireless Sensor Networks (WSNs). This system is integrated with the Global System for Mobile (GSM) communications and the protocol it uses is zigbee. The system consists of sensor nodes, a control center and data base through which sensing data can be stored for history and future plans. It is used to monitor Carbon Monoxide (CO), Sulfur Dioxide (SO۲) and dust concentration caused by industrial emissions due to process.

Keywords:

object detection model , Neural Network , Deep Learning , Python