Research on highway rain monitoring based on rain monitoring coefficient.

The real-time and accurate monitoring of severe weather is the key to reducing traffic accidents on highways. Currently, rainy day monitoring based on video images focuses on removing the impact of rain. This article aims to build a monitoring model for rainy days and rainfall intensity to achieve precise monitoring of rainy days on highways. This paper introduces an algorithm that combines the frequency domain and spatial domain, thresholding, and morphology. It incorporates high-pass filtering, full-domain value segmentation, the OTSU method (the maximum inter-class difference method), mask processing, and morphological opening for denoising. The algorithm is designed to build the rain coefficient model Prain coefficient and determine the intensity of rainfall based on the value of Prain coefficient. To validate the model, data from sunny, cloudy, and rainy days in different sections and time periods of the Jinan Bypass G2001 line were used. The aim is to raise awareness about driving safety on highways. The main findings are: the rain coefficient model Prain coefficient can accurately identify cloudy and rainy days and assess the intensity of rainfall. This method is not only suitable for highways but also for ordinary road sections. The model's accuracy has been verified, and the algorithm in this study has the highest accuracy. This research is crucial for road traffic safety, particularly during bad weather such as rain.

The spatial and temporal disaggregation models of high-accuracy vehicle emission inventory.

A high-accuracy gridding vehicle emission inventory is not only the foundation for developing refined emission control strategies but a necessary input to air quality model as well. An accurate approach to the spatiotemporal disaggregation is the key step to improving the accuracy of gridding emission inventories. The existing spatial disaggregation method considers relatively fewer impact factors, lacking adequate correlation analysis among impact factors. Additionally, the existing temporal disaggregation method does not correspond with the actual travel behavior of residents. This paper proposes a multi-factor spatial disaggregation model by principal component analysis (PCAM), based on a correlation analysis of the main impact factors. Further, a new temporal disaggregation model is proposed based on the congestion delay index combined with the traffic flow fundamental model (CDITF). The results from a case study in Jinan show that the square of correlation coefficients (RSQ) between the model- disaggregated NO2 emissions based on PCAM and the monitored NO2 concentration increased by 34.4% compared to the traditional disaggregation model based on the standard road length, and the RSQ for CO increased by 13%; the NMD and NME of the simulation results based on CMAQ model compared to standard road length model decrease by approximately 33.7% and 35.5%, respectively. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data. The PCAM method and the CDITF proposed in this paper are more in line with the actual situation using the cumulative emissions on road sections. The vehicle emissions in Jinan are found to be concentrated in the center of each district and county and near high-grade roads. The disaggregation results in areas with large road slopes are more realistic for considering road slope factors. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data, however, the monitored concentration data presents a certain degree of time lag. The proposed spatiotemporal disaggregation model in this paper improves the accuracy of gridding vehicle emission inventory, which is of a great significance for developing precise control strategies of vehicle emissions and improving the urban air quality in general.

The impacts of road traffic on urban air quality in Jinan based GWR and remote sensing.

Traffic congestion and smog are hot topics in recent years. This study analyzes the impacts of road traffic characteristic parameters on urban air quality quantitatively based on aerosol optical thickness (AOD) and geographical weighted regression (GWR) models, including the road network density, road area occupancy, intersection number, and bus network density as main factors. There are some major research findings. Firstly, there exists a strong positive correlation between the peak congestion delay index (PCDI) and air quality, the correlation has R2 values of up to 0.4962 (R 0.70). Secondly, GWR refines the local spatial changes in the AOD and the road parameters, and the correlation R2 based GWR model all above 0.6. The correlation between AOD and the road area occupancy was the highest, and the correlations with the bus network density and the intersections number were higher than that with the road network density. Thus, bus route planning, bus emission reduction, road network planning, and signal timing (at intersections) have a greater impact on air quality than other policy, especially in areas with traffic jams. The results of this study could provide theoretical support for traffic planning and traffic control, and is promising in practice.

A high-resolution index suitable for multi-pollutant monitoring in urban areas.

In view of the problems involved in remote sensing monitoring of urban air quality, including low spatial resolution, only for a single pollutant, complex inversion algorithms, and difficultly obtaining parameter values, in this study, a new difference smog index (DSI) was developed, and then a comparison with the normal difference haze index, the difference index, and the MODIS aerosol optical depth products. The results show that the DSI model developed in this study has a higher accuracy and a better monitoring effect in urban areas, and it has a higher resolution (30 m), which greatly improves the degree of refinement of the remote sensing monitoring. The DSI model has a higher extensibility, and it is suitable for monitoring the AQI, PM2.5, NO2. The DSI model proposed in this paper is simple and easy to use, and thus, it has a high potential for application and deserves promotion in urban air quality monitoring.