TY - JOUR
T1 - The MR-CA Models for Analysis of Pollution Sources and Prediction of PM 2.5
AU - Deng, Fang
AU - Ma, Liqiu
AU - Gao, Xin
AU - Chen, Jie
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - The haze problem in cities poses a great threat to human health. Although there are many factors that cause fog and haze, the main reason is the increase of the PM 2.5 concentration. Due to the complexity of the particles motion, it is difficult to use traditional methods obtain information about PM 2.5 (including its sources, influencing factors, and distribution forecast). This paper presents a cellular automata (CA) model based on a multivariate regression model and several physical models to analyze the generation and diffusion of PM 2.5 . In Beijing for example, after the researches, the multiple regression confirmed that the major source of PM 2.5 is vehicle pollution, which accounts for 39.2% of the pollution generated in Beijing. The secondary source is from the residential areas, accounting for 27.5% in the winter. Besides, 32% of the total pollution comes from nearby areas. In addition, it is also confirmed that the weather factors, such as, temperature, wind, pressure and relative humidity, and radiation are all have a great impact on PM 2.5 . The CA model is demonstrated to be an effective simulation and prediction method for PM 2.5 since it allows for the estimation of the governance results by simulating the control scheme and predicting the concentration of PM 2.5 accurately in the next 48 h. In the prediction experiment, 77.5% of prediction error is less than 20 μg/m 3 .
AB - The haze problem in cities poses a great threat to human health. Although there are many factors that cause fog and haze, the main reason is the increase of the PM 2.5 concentration. Due to the complexity of the particles motion, it is difficult to use traditional methods obtain information about PM 2.5 (including its sources, influencing factors, and distribution forecast). This paper presents a cellular automata (CA) model based on a multivariate regression model and several physical models to analyze the generation and diffusion of PM 2.5 . In Beijing for example, after the researches, the multiple regression confirmed that the major source of PM 2.5 is vehicle pollution, which accounts for 39.2% of the pollution generated in Beijing. The secondary source is from the residential areas, accounting for 27.5% in the winter. Besides, 32% of the total pollution comes from nearby areas. In addition, it is also confirmed that the weather factors, such as, temperature, wind, pressure and relative humidity, and radiation are all have a great impact on PM 2.5 . The CA model is demonstrated to be an effective simulation and prediction method for PM 2.5 since it allows for the estimation of the governance results by simulating the control scheme and predicting the concentration of PM 2.5 accurately in the next 48 h. In the prediction experiment, 77.5% of prediction error is less than 20 μg/m 3 .
KW - Environmental prediction system
KW - PM problem
KW - evaluation of control scheme
KW - pollution source analysis
UR - http://www.scopus.com/inward/record.url?scp=85028943299&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2017.2721100
DO - 10.1109/TSMC.2017.2721100
M3 - Article
AN - SCOPUS:85028943299
SN - 2168-2216
VL - 49
SP - 814
EP - 820
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 4
M1 - 7982692
ER -
