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Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone (TC). However, it is difficult to be fully represented in regional models due to domain size and a lack of observation data, particularly at sea used in regional data assimilation. Blending analysis has been developed and implemented in regional models to reintroduce large-scale information from global model to regional analysis. Research of the impact of this large-scale blending scheme for the Global / Regional Assimilation and PrEdiction System (GRAPES) model on TC forecasting is limited and this study attempts to further progress by examining the adaptivity of the blending scheme using the two-dimensional Discrete Cosine Transform (2D-DCT) filter on the model forecast of Typhoon Haima over Shenzhen, China in 2016 and considering various cut-off wavelengths. Results showed that the error of the 24-hour typhoon track forecast can be reduced to less than 25 km by applying the scale-dependent blending scheme, indicating that the blending analysis is effectively able to minimise the large-scale bias for the initial fields. The improvement of the wind forecast is more evident for u-wind component according to the reduced root mean square errors (RMSEs) by comparing the experiments with and without blending analysis. Furthermore, the higher equitable threat score (ETS) provided implications that the precipitation prediction skills were increased in the 24h forecast by improving the representation of the large-scale feature in the GRAPES analysis. Furthermore, significant differences of the track error forecast were found by applying the blending analysis with different cut-off wavelengths from 400 km to 1200 km and the track error can be reduced less than by 10 km with 400 km cut-off wavelength in the first 6h forecast. It highlighted that the blending scheme with dynamic cut-off wavelengths adapted to the development of different TC systems is necessary in order to optimally introduce and ingest the large-scale information from global model to the regional model for improving the TC forecast. In this paper, the methods and data applied in this study will be firstly introduced, before discussion of the results regarding the performance of the blending analysis and its impacts on the wind and precipitation forecast correspondingly, followed by the discussion of the effects of different blending scheme on TC forecasts and the conclusion section. [ABSTRACT FROM AUTHOR]

With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection's queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively. [ABSTRACT FROM AUTHOR]

Halogenated polycyclic aromatic hydrocarbons (HPAHs) have been reported to occur widely in urban air. Nevertheless, knowledge about the human health risk associated with inhalation exposure to HPAHs is scarce so far. In the present study, nine HPAHs and 16 PAHs were determined in atmospheric particulate matter (PM) collected from Shenzhen, China to address this issue. Concentrations of Σ9 HPAHs varied from 0.1 to 1.5 ng/m3 and from 0.09 to 0.4 ng/m3 in PM10 and PM2.5 samples, respectively. As for individuals, 9-bromoanthracene, 7-bromobenz(a)anthracene, and 9,10-dibromoanthracene were the dominant congeners. Levels of Σ16 PAHs in PM10 and PM2.5 samples ranged from 3.2 to 81 ng/m 3 and from 2.8 to 85 ng/m3, respectively. Among individual PAHs, chrysene, benzo[b]fluoranthene, and indeno[1,2,3-c,d]pyrene were the main congeners. According to the season, concentrations of HPAHs and PAHs in atmospheric PM10/PM2.5 samples show a similar decreasing trend with an order: winter>autumn>spring>summer. The daily intake (DI) of PM10/PM2.5-bound HPAHs and PAHs were estimated. Our results indicated that children have the highest DI levels via inhalation exposure. The incremental lifetime cancer risk (ILCR) induced by PM10/PM2.5-bound HPAHs and PAHs were calculated. The ILCR values showed a similar decreasing trend with an order: adults>children>seniors>adolescent. Overall, the ILCR values induced by HPAHs and PAHs were far below the priority risk level (10 −4 ), indicating no obvious cancer risk. To our knowledge, this is the first study to investigate the human health risk associated with inhalation exposure to PM10/PM2.5-bound HPAHs. [ABSTRACT FROM AUTHOR]