Severe haze pollution that occurred in January 2014 in Wuhan was investigated. The factors leading to Wuhan’s PM2.5 pollution and the characteristics and formation mechanism were found to be significantly different from other megacities, like Beijing. Both the growth rates and decline rates of PM2.5 concentrations in Wuhan were lower than those in Beijing, but the monthly PM2.5 value was approximately twice that in Beijing. Furthermore, the sharp increases of PM2.5 concentrations were often accompanied by strong winds. A high-precision modeling system with an online source-tagged method was established to explore the formation mechanism of five haze episodes. The long-range transport of the polluted air masses from the North China Plain (NCP) was the main factor leading to the sharp increases of PM2.5 concentrations in Wuhan, which contributed 53.4% of the monthly PM2.5 concentrations and 38.5% of polluted days. Furthermore, the change in meteorological conditions such as weakened winds and stable weather conditions led to the accumulation of air pollutants in Wuhan after the long-range transport. The contribution from Wuhan and surrounding cities to the PM2.5 concentrations was determined to be 67.4% during this period. Under the complex regional transport of pollutants from surrounding cities, the NCP, East China, and South China, the five episodes resulted in 30 haze days in Wuhan. The findings reveal important roles played by transregional and intercity transport in haze formation in Wuhan. 相似文献
Tsunami evacuation is an effective way to save lives from the near-field tsunami. Realistic evacuation simulation can provide valuable information for accurate evacuation risk assessment and effective evacuation planning. Agent-based modeling is ideal for tsunami evacuation simulation due to its capability of capturing the emergent phenomena and modeling the individual-level interactions among agents and the agents’ interactions with the environment. However, existing models usually neglect or simplify some important factors and/or mechanisms in tsunami evacuation. For example, uncertainties in seismic damages to the transportation network are not probabilistically considered (e.g., by simply removing the damaged links (roads/bridges) from the network). Typically a relatively small population (i.e., evacuees) is considered (due to computational challenges) while neglecting population mobility. These simplifications may lead to inaccurate estimation of evacuation risk. Usually, only single traffic mode (e.g., on foot or by car) is considered, while pedestrian speed adjustment and multi-modal evacuation (e.g., on foot and by car) are not considered concurrently. Also, pedestrian–vehicle interaction is usually neglected in the multi-modal evacuation. To address the above limitations, this study proposes a novel and more realistic agent-based tsunami evacuation model for tsunami evacuation simulation and risk assessment. Uncertainties in seismic damages to all links in the transportation network as well as uncertainties in other evacuation parameters are explicitly modeled and considered. A novel and more realistic multi-modal evacuation model is proposed that explicitly considers the pedestrian–vehicle interaction, walking speed variability, and speed adjustment for both the pedestrian and car according to traffic density. In addition, several different population sizes are used to model population mobility and its impact on tsunami evacuation risk. The proposed model is applied within a simulation-based framework to assess the tsunami evacuation risk assessment for Seaside, Oregon.
