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Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2.5 concentration series into a number of subsets with different frequencies. Secondly, VMD is applied to decompose each subset into a set of variational modes (VMs). Thirdly, DE-BP model is utilized to forecast all the VMs. Fourthly, the forecast value of each subset is obtained through aggregating the forecast results of all the VMs obtained from VMD decomposition of this subset. Finally, the final forecast series of PM2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM2.5 concentration series collected from Wuhan and Tianjin, respectively, located in China are used to test the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms all the other considered models in this paper.

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With the monthly data of WTI oil price and economic policy uncertainty (EPU) of China from January 2000 to August 2020, this paper detailedly investigates the asymmetric volatility correlations between two types of EPU of China and global oil price in different time scales. The empirical results demonstrate that the volatility correlation between EPU of China and West Texas Intermediate (WTI) oil price is mainly reflected in the monetary policy uncertainty (MPU), while that of fiscal policy uncertainty (FPU) is much weaker. Specifically speaking, the volatility correlation between MPU of China and downward WTI oil price is significantly negative in the short-middle term (4–8 months) and changes to positive in the middle-long term (8–16 months), while that of upward WTI oil price is only significantly positive in the long term (16–32 months). Our findings provide a deeper understanding of the oil price-EPU correlation in China, and can be valuable guidance for diversified market participants such as government policy-makers and global investors.