In this paper, a novel method was developed to orientate and quantify the air pollution sources based on remotely sensed aerosol data and Glowworm Swarm Optimization (GSO). In practice, based on source apportionment technique, the air pollution sources could just be identified to certain industries, such as transportation, power plants, biomass burning, and et.al. To our knowledge, the problem of orientating and quantifying the pollution to the individual factories is faced for the first time. In this study, the aerosol retrieved from remotely sensed image (MODIS) and GIS were used to locate and quantify the pollution to each enterprise in the study area based on an improved Glowworm Swarm Optimization and meteorological condition. As a result, the polluting contribution of each factory were be listed, and the most polluting factories were be found. Some experiments were carried out to validate the method, and the Key monitoring factories by authority was ferreted out accurately.

In this paper, the performance of a distributed Space-Time coded hybrid satellite-terrestrial cooperative network (DSTC-HSTCN) is investigated. In particular, we address the problem in a downlink distributed Alamouti coded satellite system with a single fixed terrestrial relay adopting amplify-and- forward protocol. By modeling the satellite- relay/destination and relay-destination links as Shadowed-Rician and Rayleigh distributions, respectively, we derive a closed-form expression for average symbol error rate (SER) with quadrature phase shift keying modulation. Simulation results verify the validity of the theoretical analysis and show the performance improvement of the hybrid satellite- terrestrial cooperative network (HSTCN).