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
根据GABLS(Global Energy and Water Cycle Experiment Atmospheric Boundary Layer Study)的第2个个例在GRAPES(Global and Regional Assimilation and PrEdiction System)单柱模式中构造了一个试验,用于检验规定的下垫面温度强迫条件下边界层过程的昼夜循环模拟能力。然后,将模拟结果与观测和大涡模拟结果进行了比较。结果表明,在规定的下垫面温度强迫下,GRAPES模拟的2m温度基本合理。然而,对于稳定条件(夜间),GRAPES模拟的向下的感热通量比观测的大,过估10m风速和摩擦速度,过估稳定边界层高度;对于不稳定条件(白天),GRAPES模拟的向上的感热通量比观测的小,低估不稳定边界层高度,低层位温偏冷。随后的敏感试验表明,减小边界层方案中的动量和热量的背景扩散值后,GRAPES模拟的稳定条件下的10m风速和摩擦速度,以及对流边界层的风和温度的廓线更接近大涡模拟。 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.
The changes in a selection of extreme climate indices(maximum of daily maximum temperature(TXx),minimum of daily minimum temperature(TNn),annual total precipitation when the daily precipitation exceeds the 95th percentile of wet-day precipitation(very wet days,R95p),and the maximum number of consecutive days with less than 1 mm of precipitation(consecutive dry days,CDD))were projected using multi-model results from phase 5 of the Coupled Model Intercomparison Project in the early,middle,and latter parts of the 21st century under different Representative Concentration Pathway(RCP)emissions scenarios.The results suggest that TXx and TNn will increase in the future and,moreover,the increases of TNn under all RCPs are larger than those of TXx.R95p is projected to increase and CDD to decrease significantly.The changes in TXx,TNn,R95p,and CDD in eight sub-regions of China are different in the three periods of the 21st century,and the ranges of change for the four indices under the higher emissions scenario are projected to be larger than those under the lower emissions scenario.The multi-model simulations show remarkable consistency in their projection of the extreme temperature indices,but poor consistency with respect to the extreme precipitation indices.More substantial inconsistency is found in those regions where high and low temperatures are likely to happen for TXx and TNn,respectively.For extreme precipitation events(R95p),greater uncertainty appears in most of the southern regions,while for drought events(CDD)it appears in the basins of Xinjiang.The uncertainty in the future changes of the extreme climate indices increases with the increasing severity of the emissions scenario. 相似文献