The MR-CA Models for Analysis of Pollution Sources and Prediction of PM2.5

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
$20~ {\mu }\text{g} / {\text {m}}^{3}$
.