Continuous air pollution (CAP) incidents last even longer and generate greater health hazards relative to conventional air pollution episodes. However, few studies have focused on the spatiotemporal distribution characteristics and driving factors of CAP in China. Drawing on the daily reported ground monitoring data on the ambient air quality in 2019 in China, this paper identifies the spatiotemporal distribution characteristics of CAP across 337 Chinese cities above the prefecture level using descriptive statistics and spatial statistical analysis methods, and further examines the spatial heterogeneity effects of both socioeconomic factors and natural factors on CAP with a Multiscale Geographically Weighted Regression (MGWR) model. The results show that the average proportion of CAP days in 2019 reached 11.50% of the whole year across Chinese cities, a figure equaling to about 65 days, while the average frequency, the maximum amount of days and the average amount of days of CAP were 8.02 times, 7.85 days and 4.20 days, respectively. Furthermore, there was a distinct spatiotemporal distribution disparity in CAP in China. Spatially, the areas with high proportions of CAP days were concentrated in the North China Plain and the Southwestern Xinjiang Autonomous Region in terms of the spatial pattern, while the proportion of CAP days showed a monthly W-shaped change in terms of the temporal pattern. In addition, the types of regions containing major pollutants during the CAP period could be divided into four types, including “Composite pollution”, “O3 + NO2 pollution”, “PM10 + PM2.5 pollution” and “O3 + PM2.5 pollution”, while the region type “PM10 + PM2.5 pollution” covered the highest number of cities. The MGWR model, characterized by multiple spatial scale impacts among the driving factors, outperformed the traditional OLS and GWR model, and both socioeconomic factors and natural factors were found to have a spatial non-stationary relationship with CAP in China. Our findings provide new policy insights for understanding the spatiotemporal distribution characteristics of CAP in urban China and can help the Chinese government make prevention and control measures of CAP incidents.

No abstract

We consider wireless multi-hop networks in which each node aims to securely transmit a message. To guarantee the secure transmission, we employ an independent randomization encoding strategy to encode the confidential message. We aim to maximize the network utility. Based on the finite length of a secrecy codewords strategy, we develop an improved control algorithm, subject to network stability and secrecy outage requirements. On the basis of the Lyapunov optimization method, we design an control algorithm, which is decomposed into end-to-end secrecy encoding, flow control and routing scheduling. The simulation results show that the proposed algorithm can achieve a utility result that is arbitrarily close to the optimal value. Finally, the performance of the proposed control policy is validated with various network conditions.