Search

High-quality visibility forecasting benefits traffic transportation safety, public services, and tourism. For a more accurate forecast of the visibility in the Guizhou region of China, we constructed several visibility forecasting models via progressive refinements in different compositions of input observational variables and the adoption of the Unet architecture to perform hourly visibility forecasts with lead times ranging from 0 to 72 h over Guizhou, China. Three Unet-based visibility forecasting models were constructed according to different inputs of meteorological variables. The model training via multiple observational variables and visibility forecasts of a high-spatiotemporal-resolution numerical weather prediction model (China Meteorological Administration, Guangdong, CMA-GD) produced a higher threat score (TS), which led to substantial improvements for different thresholds of visibility compared to CMA-GD. However, the Unet-based models had a larger bias score (BS) than the CMA-GD model. By introducing the U2net architecture, there was a further improvement in the TS of the model by approximately a factor of two compared to the Unet model, along with a significant reduction in the BS, which enhanced the stability of the model forecast. In particular, the U2net-based model performed the best in terms of the TS below the visibility threshold of 200 m, with a more than eightfold increase over the CMA-GD model. Furthermore, the U2net-based model had some improvements in the TS, BS, and RMSE (root-mean-square error) compared to the LSTM_Attention model. The spatial distribution of the TS showed that the U2net-based model performed better at the model grid scale of 3 km than at the scale of individual weather stations. In summary, the visibility forecasting model based on the U2net algorithm, multiple observational variables, and visibility data from the CMA-GD model performed the best. The compositions of input observational variables were the key factor in improving the deep learning model’s forecasting capability, and these improvements could improve the value of forecasts and support the socioeconomic needs of sectors reliant on visibility forecasting.

Adjustable-width pulse signals are widely used in systems such as test equipment for hold time, response time and radar testing. In this study, we proposed a pulse generation method based on virtual sampling with ultra-high pulse width resolution. In the proposed method, the sampling rate of a digital-to-analogue converter (DAC) was adjusted to considerably improve pulse width resolution. First, the sampling rate was matched with the target pulse width resolution to digitally sample the ideal signal and generate digital waveform sampling points. Next, the signal bandwidth of the DAC was matched using a low-pass digital filter. Finally, the waveform sampling points were downsampled using an integer factor and output after digital-to-analogue conversion. The waveform pulse width information generated by high-frequency digital sampling was passed step by step and retained in the final output analogue signal. A DAC with a sampling rate of 1.25 GSa/s was used, and the pulse width resolution of the pulse signal was 0.1 ns. Theoretically, a sampling rate of 10 GSa/s is required to achieve 0.1 ns resolution. This method is simple, has a low cost, and exhibits excellent performance.

Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model’s construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events.

Accurate precipitation nowcasting (lead time: 0–2 h), which requires high spatiotemporal resolution data, is of great relevance in many weather-dependent social and operational activities. In this study, we are aiming to construct highly accurate deep learning (DL) models to directly obtain precipitation nowcasting at 6-min intervals for the lead time of 0–2 h. The Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN) models were used as comparative DL models, and the Lucas–Kanade (LK) Optical Flow method was selected as a traditional extrapolation baseline. The models were trained with high-quality datasets (resolution: 1 min) created from precipitation observations recorded by automatic weather stations in Guizhou Province (China). A comprehensive evaluation of the precipitation nowcasting was performed, which included consideration of the root mean square error, equitable threat score (ETS), and probability of detection (POD). The evaluation indicated that the reduction of the number of missing values and data normalization boosted training efficiency and improved the forecasting skill of the DL models. Increasing the time series length of the training set and the number of training samples both improved the POD and ETS of the DL models and enhanced nowcasting stability with time. Training with the Hea-P dataset further improved the forecasting skill of the DL models and sharply increased the ETS for thresholds of 2.5, 8, and 15 mm, especially for the 1-h lead time. The PredRNN model trained with the Hea-P dataset (time series length: 8 years) outperformed the traditional LK Optical Flow method for all thresholds (0.1, 1, 2.5, 8, and 15 mm) and obtained the best performance of all the models considered in this study in terms of ETS. Moreover, the Method for Object-Based Diagnostic Evaluation on a rainstorm case revealed that the PredRNN model, trained well with high-quality observation data, could both capture complex nonlinear characteristics of precipitation more accurately than achievable using the LK Optical Flow method and establish a reasonable mapping network during drastic changes in precipitation. Thus, its results more closely matched the observations, and its forecasting skill for thresholds exceeding 8 mm was improved substantially.

Benzene, toluene, and xylene (denoted as BTX) are normally used in coatings, sealants, curing agents and other home decoration products, which can cause harm to human health. However, traditional studies mostly focus on the toxicity evaluation of a single pollution source, and little attention has been paid to the toxicity reports of multiple pollutants in a complex system. To evaluate the impact of indoor BTX on human health at the cellular level, the oxidative stress effect of BTX on human bronchial epithelial cells was assessed, including cell cytotoxicity, intracellular ROS, cell mitochondrial membrane potential, cell apoptosis, and CYP2E1 expression. The concentrations of BTX introduced into the human bronchial epithelial cell culture medium were determined based on both the tested distribution in 143 newly decorated rooms and the limited concentrations in the indoor air quality (denoted as IAQ) standards. Our study showed that the concentration in line with the standard limit may still pose a serious risk to health. The cellular biology effect studies of BTX showed that BTX, even at concentrations lower than the national standard limit, can still induce observable oxidative stress effects which warrant attention.

The fabrication of a coffee ring was studied in this work to improve its sensitivity in detecting trace analytes based on surface-enhanced Raman scattering (SERS). Gold nanoparticles were synthesized with diameters of ~40 nm through the sodium citrate reduction method, and rhodamine 6G (R6G) was employed as a probe to evaluate the performance of the fabricated coffee rings. The results showed that the coffee ring formed from the water-washed gold nanoparticles presented more orderly and regular morphology as well as better SERS properties than the unwashed ones. Furthermore, both the concentration and the amount of gold nanoparticles were found to affect its SERS performance. Using the optimized coffee ring as a SERS substrate, trace R6G with a concentration of 5 × 10−8 M was detected. This sensing platform could realize aflatoxin B1 (AFB1) detection down to 5 × 10−7 M and was demonstrated to function well in real-sample testing.

In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at the 10 m and multiple isobaric surfaces (500 hPa, 700 hPa, 850 hPa and 925 hPa) over North China for 2020. The straightforward multimodel ensemble mean (MME) method is utilized to improve forecasting abilities. In addition, the forecast errors are decomposed to further diagnose the error sources of wind forecasts. Results indicated that there is little difference in the performances of the four models in terms of wind direction forecasts (DIR), but obvious differences occur in the meridional wind (U), zonal wind (V) and wind speed (WS) forecasts. Among them, the ECMWF and NCEP showed the highest and lowest abilities, respectively. The MME effectively improved wind forecast abilities, and showed more evident superiorities at higher levels for longer lead times. Meanwhile, all of the models and the MME manifested consistent trends of increasing (decreasing) errors for U, V and WS (DIR) with rising height. On the other hand, the main source of errors for wind forecasts at both 10 m and isobaric surfaces was the sequence component (SEQU), which rose rapidly with increasing lead times. The deficiency of the less proficient NCEP model at the 10 m and isobaric surfaces could mainly be attributed to the bias component (BIAS) and SEQU, respectively. Furthermore, the MME tended to produce lower SEQU than the models at all layers, which was more obvious at longer lead times. However, the MME showed a slight deficiency in reducing BIAS and the distribution component of forecast errors. The results not only recognized the model forecast performances in detail, but also provided important references for the use of wind forecasts in business departments and associated scientific researches.

Purpose: The combination therapy of rosuvastatin (RSV) and the platelet inhibitor clopidogrel (CP) is widely accepted in the management of cardiovascular diseases. The objective of the present study was to identify the mechanism of RSV–CP DDI and evaluate the risk of hepatotoxicity associated with the concomitant use of CP.Methods: We first studied the effect of CP and its major circulating metabolite, carboxylic acid metabolite (CPC), on RSV transport by overexpressing cells and membrane vesicles. Second, we investigated whether a rat model could replicate this DDI and then be used to conduct mechanistic studies and assess the risk of hepatotoxicity. Then, cytotoxicity assay in hepatocytes, biochemical examination, and histopathology were performed to measure the magnitude of liver injury in the presence and absence of DDI.Results: CP inhibited OATP1B1-mediated transport of RSV with an IC50 value of 27.39 μM. CP and CPC inhibited BCRP-mediated RSV transport with IC50 values of