1. Title, Journal and Authors

Title : Developing an early-warning system for air quality predicition and assessment of cities in China

Authors : Jianzhou Wang^a, Xiaobo Zhang^a,*, Zhenhai Guo^b, Haiyan Lu^c

^aSchool of Stastistics, Dongbei University of Finance and Economics, Dalian 116025, China

^bState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy

of Sciences, Beijing 100029, China

^cSchool of Software, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia

^*Corresponding author

E-mail addresses : wangjz@dufe.edu.cn(J. Wang), zxb100498@163.com(X. Zhang), gzh@lasg.jap.ac.cn(Z. Guo), haiyan.lu@uts.edu.au(H. lu) 

 

2. Summary

 

 In recent years, air pollution has received iincreasing attention due to the negative effects. For this reason, monitoring the air quality has received attention from both managers and citizens. Most of the previous studies of air pollution have focused on forecasting the concentrations of atmospheric pollutants and air quality assessment. In this report, the resarch team attempt to do develope the new early-warning system which is composed of air quality predicition and assesment modules. This robust integrated module consists of 3 stages. First stage is the pre-analysis. Using artificial intelligent optimization algorithms and CS optimization algorithm, this stage gives the statistical charascteristics of pollutant emissions. Next stage is the forecasting. In this stage, the neural network pr-edictor is employed to forecast the pollutant concentrations. using the historical data and the optimal unimodal model from the previous stage. The last stage is the air quality assessment which placed on the second module, unlike the other two. Fuzzy set theory and the Analytic Hierachy process are used in the second modul-e to reflect the air pollutant condition more clearly. 

 To verify the effectiveness of the system, They conduct the experiment at the 2 cities from China, Chengdu and Hangzhou, using data from the Ministry of Enviromental Protection of China. The results demonstrate that the new methods are reliable and suitable for use by environmental supervisiors in air pollution monitoring and management.  

 

 

 

 

3. Contact

 

Sungryul Kim / Intern student

 

Environmental Systems Engineering Lab.

 

Gwangju Institute of Science and Technology

 

 

 

Phone : +82-10-2788-2169

 

E-mail : fuf1994@naver.com