Wet deposition, including both in- and below-cloud scavenging, is critical for the atmospheric transport modeling of ¹³⁷Cs following the Fukushima Daiichi Nuclear power plant (FDNPP) accident. Although intensively investigated, wet deposition simulation is still subject to uncertainties of meteorological inputs and wet scavenging modeling, leading to biased ¹³⁷Cs transport prediction. To reduce the dual uncertainties, in- and below-cloud wet scavenging schemes of ¹³⁷Cs were simultaneously integrated into Weather Research and Forecasting-Chemistry (WRF-Chem), yielding online coupled modeling of meteorology and the two wet scavenging processes. The integration was performed using 25 combinations of different in- and below-cloud schemes, covering most schemes in the literature. Two microphysics schemes were also tested to better reproduce the precipitation. The 25 models and the ensemble mean of 9 representative models were systematically compared with the below-cloud-only WRF-Chem model, using the cumulative deposition and atmospheric concentrations of ¹³⁷Cs measurements. The results reveal that, with the Morrison's double moment cloud microphysics scheme, the developed models could better reproduce the rainfall and substantially improve the cumulative deposition simulation. The in-cloud scheme is influential to the model behaviors and those schemes considering cloud parameters also improve the atmospheric concentration simulations, whereas the others solely dependent on the rain intensity are sensitive to meteorology. The ensemble mean achieves satisfactory performance except one plume event, but still outperforms most models.

Beijing experiences frequent PM_2.5 pollution, which is influenced by the planetary boundary layer (PBL) structure/process. Partly due to a lack of appropriate observations, the impacts of PBL on PM_2.5 pollution are not yet fully understood. Combining wind-profiler data, radiosonde measurements, near-surface meteorological observations, aerosol measurements, and three-dimensional simulations, this study investigated the influence of PBL structure and the low-level jet (LLJ) on the pollution in Beijing from 19 to 20 September 2015. The evolution of the LLJ was generally well simulated by the model, although the wind speed within the PBL was overestimated. Being influenced by the large-scale southerly prevailing winds, the aerosols emitted from the southern polluted regions could be easily transported to Beijing, contributing to ~68% of the PM_2.5 measured in Beijing on 20 September. The relative contribution of external transport of PM_2.5 to Beijing was high in the afternoon (≥80%), which was related to the strong southerly PBL winds and the presence of thermally-induced upslope winds. On 20 September, the LLJ in Beijing demonstrated a prominent diurnal variation, which was predominant in the morning and after sunset. The occurrence of the LLJ could enhance the dilution capacity in Beijing to some extent, which favors the dilution of pollutants at a local scale. This study has important implications for better understanding the complexity of PBL structure/process associated with PM_2.5 pollution in Beijing.

Influenza causes repeat epidemics and huge loss of lives and properties. To predict influenza epidemics, we proposed an infectious disease dynamic prediction model with control variables (SEIR-CV), which considers the characteristics of the influenza epidemic transmission, seasonal impacts, and the intensity changes of control measures over time. The critical parameters of the model were inversed using an adjoint method. When using the surveillance data of the past 15 weeks to invert the parameters, the epidemic in the next 3 weeks in the United States can be accurately predicted. In addition, roll predictions from 26 September 2016 to 27 September 2018 were implemented. The correlation coefficient between the predicted values and the surveillance values was greater than 0.975, and the overall relative error of the predictions was less than 10%. These good model performances demonstrated the practicability and feasibility of SEIR-CV for influenza and corresponding infectious disease prediction.

It is key to assess quantitatively the transportation risks of hazardous materials for optimal route. It's difficult to analyze efficiently transportation risk involved the factors such as meteorological conditions, the underlying properties and sensitivity to harm. So the adjoint method is used to analyze transportation risks, the adjoint equation to calculate poisoning dose is deduced based on advection diffusion equation, the transportation risks , individual risks and social risks integrated with traditional risk model and principles of probability statistics were respectively analyzed, and penalty function is used to handle the constraint condition. The case suggested the feasibility of this method. In this paper, the dynamic model is proposed, which reflected the correlation between these factors such as meteorological conditions, topography conditions, toxicity of hazardous materials and transportation risks. The adjoint method is developed to use to solve this problem. These basic theories are meaningful for risk control of dangerous chemicals transportation.