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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 PM2.5 concentration. Due to the complexity of the particles motion, it is difficult to use traditional methods obtain information about PM2.5 (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 PM2.5. In Beijing for example, after the researches, the multiple regression confirmed that the major source of PM2.5 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 PM2.5. The CA model is demonstrated to be an effective simulation and prediction method for PM2.5 since it allows for the estimation of the governance results by simulating the control scheme and predicting the concentration of PM2.5 accurately in the next 48 h. In the prediction experiment, 77.5% of prediction error is less than $20~ {\mu }\text{g} / {\text {m}}^{3}$. [ABSTRACT FROM AUTHOR]

For the safe operation of nuclear power plants (NPPs), it is significant to promptly and correctly identify malfunctions/transients, which is difficult for the operators by monitoring the variation of important parameters. In this paper, a novel method is proposed for the transient identification in NPPs. It directly models the transients in a linear space that is represented by a matrix instead of a training process. Then a transient can be represented via a linear combination of the columns of the modeling matrix, and the coefficient vector encodes the identity of the transient. To obtain the coefficient vector, the smoothed \ell^0-norm optimization (SL0) algorithm is adopted, where the truncated singular value decomposition (TSVD) method is combined to improve the stability. Then the identification of a transient can be accomplished by examining the property of the solution using three quantities. The proposed method can correctly classify the transients and successfully reject “unknown” types, and it is applicable under a wide range of operational conditions. It is verified by simulator data of pebble-bed modular high temperature gas-cooled reactor nuclear power plant (HTR-PM) developed by Institute of Nuclear and New Energy Technology with Tsinghua University. [ABSTRACT FROM PUBLISHER]