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Haze occurs frequently in many cities, and becomes the authors' great common concern. A distributed real-time monitoring system for atmospheric particles has been designed, implemented and tested. The proposed system consists of a front-end data wireless acquisition network, an embedded web server system, a central server and remote monitoring terminals. The front-end data-acquisition network is made up of mobile data-acquisition nodes distributed in atmospheric particle monitoring areas which collect atmospheric particle data periodically. The data is transmitted by Jennet wireless network and sent to the Internet through the embedded web servers attached to the coordinator nodes via the serial ports. Only a common Internet browser is required for a remote user to check the atmospheric particle data. The central server is responsible for storing the pollution data for further usage of pollution analysis, evaluation and early warning. In this study, the PM1.0, PM2.5 and PM10 particle data were successfully collected, transmitted and checked by the monitoring system in Lanzhou, China. The results show that the designed system can meet the needs of functional requirements of atmospheric particle monitoring.

To meet the requirements of data acquisition from mobile pollution sources and unfixed data acquisition points in atmospheric particle monitoring system, this study designs a real-time monitoring system for atmospheric particles such as PM2.5 based on a single-chip microcomputer, an ET-iLink open cloud platform and an Android operating system. The movable data acquisition nodes for atmospheric particles are designed. The laser scattering method is used to obtain the data of atmospheric particle concentration, which are uploaded to the cloud platform through the GPRS network. The Android application obtains the real time data of atmospheric particle concentration from the cloud platform. Thus the real-time remote monitoring for atmospheric particles is realised. The test results show that the system has the following advantages: stable performance, high scalability and low error rate.

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