We assessed the ability of the MM5/CMAQ model to predict ozone (O3) air quality over the Kanto area and to investigate the factors
that a ect simulation of O3. We find that the coupled MM5/CMAQ model is a useful tool for the analysis of urban environmental
problems. The simulation results were compared with observational data and were found to accurately replicate most of the important
observed characteristics. The initial and boundary conditions were found to have a significant e ect on simulated O3 concentrations.
The results show that on hot and dry days with high O3 concentration, the CMAQ model provides a poor simulation of O3 maxima when
using initial and boundary conditions derived from the CMAQ default data. The simulation of peak O3 concentrations is improved with
the JCAP initial and boundary conditions. On mild days, the default CMAQ initial and boundary conditions provide a more realistic
simulation. Meteorological conditions also have a strong impact on the simulated distribution and accumulation of O3 concentrations
in this area. Low O3 concentrations are simulated during mild weather conditions, and high concentrations are predicted during hot
and dry weather. By investigating the e ects of di erent meteorological conditions on each model process, we find that advection and
di usion di er the most between the two meteorological regimes. Thus, di erences in the winds that govern the transport of O3 and its
precursors are likely the most important meteorological drivers of ozone concentration over the central Kanto area. 相似文献
Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.
Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).
Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.
Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy. 相似文献