A particulate matter (PM) transport model is developed to investigate coarse PM (PM 10 ), fine PM (PM 2.5 ), and very fine PM (PM 1 ) transport mechanisms in urban street canopies under low-wind conditions. Two common building layouts (i.e., the open and staggered street canopies) are considered. Large eddy simulations with the subgrid-scale stress model and the wall function are used to simulate urban streetcanopy flows. The Lagrangian particle tracking approach, considering the effects of the drag force, gravitational force, Brownian motion, and Saffman lift force on particles is adopted to study PM transport behaviors in urban street canopies. The box counting method is used to calculate the canopy-averaged PM 10 /PM 2.5 /PM 1 mass concentrations and transport mechanisms at each tracking time. The simulated results show that the removal efficiencies of PM 10 , PM 2.5 , and PM 1 in the open street canopies are all better than those in the staggered street canopies. As a result, the open street canopies having higher PM removal ability lead to a swifter shift of the particle size distributions towards smaller size and less deviation than the staggered street canopies. The major particle removal mechanism for the open street canopies is particle escape, whereas wall deposition plays the most important role for the staggered street canopies. In comparison with the effectiveness of PM 10 /PM 2.5 /PM 1 removal for both building layouts, PM 10 particles are easier to overcome the root mean square vertical turbulent velocity and need less time to deposit. Fine particles would follow airflow paths and need longer time to deposit. As a result, PM 2.5 and PM 1 are more difficult to be removed than PM 10 .
Rainfall during typhoons is one of the most important water resources in Taiwan, but heavy typhoon rainfall often leads to serious disasters and consequently results in loss of lives and property. Hence, accurate forecasts of typhoon rainfall are always required as important information for water resources management and rainfall-induced disaster warning system. In this study, a methodology is proposed for providing quantitative forecasts of 24 h cumulative rainfall during typhoons. Firstly, ensemble forecasts of typhoon rainfall are obtained from an ensemble numerical weather prediction (NWP) system. Then, an evolutionary algorithm, i.e., genetic algorithm (GA), is adopted to real-time decide the weights for optimally combining these ensemble forecasts. That is, the novelty of this proposed methodology is the effective integration of the NWP-based ensemble forecasts through an evolutionary algorithm-based strategy. An actual application is conducted to verify the forecasts resulting from the proposed methodology, namely NWP-based ensemble forecasts with a GA-based integration strategy. The results confirm that the forecasts from the proposed methodology are in good agreement with observations. Besides, the results from the GA-based strategy are more accurate as compared to those by simply averaging all ensemble forecasts. On average, the root mean square error decreases about 7%. In conclusion, more accurate typhoon rainfall forecasts are obtained by the proposed methodology, and they are expected to be useful for disaster warning system and water resources management during typhoons.
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