TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide), water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.@en

To increase the efficiency and safety of expressway, this paper constructed a new intelligent connected cooperative vehicle infrastructure system and its effectiveness was verifid from both data and practical applications. Firstly, considering the convenience of using intelligent networking systems for public transportation, a new intelligent connected cooperative vehicle infrastructure system architecture was proposed by incorporating mobile communication methods. Then, the new system was illustrated from road side unit (RSU), on board unit (OBU) and data interaction. Additionally, to verify the effectiveness of the system, this paper proposes a two-stage model named Transformer Embedded Clustering- Hierarchical Density-Based Spatial Clustering of Applications with Noise (TEC-HDBSCAN) model to identify outliers in the trajectory data of vehicles collected by the system and obtain the speed sequence of the vehicle. Finally, data from actual testing scenarios was collected and a Best Worst Method-Improved Gray Relational (BWM-IGR) model was built to verify the effectiveness of the system. The results show that the established intelligent networked transportation system can effectively guide vehicles and collect data with high accuracy.@en

In the present study, we investigate blood flow and mass transfer of the low-density lipoprotein (LDL) in a simplified axisymmetric geometry with a mathematically well-defined narrowing (stenosis), which mimics a diseased human coronary artery. The interior of the arterial wall is represented as a porous media containing multi-layered structures of different thickness. This multi-layered structure includes anatomically realistic sublayers: endothelium, intima, internal elastic layer (IEL), media and adventitia. The coupling between the blood flow and mass transfer of LDL in the lumen (interior of artery) and arterial wall is established through a multipore model at the lumen/endothelium interface. This multipore model takes into consideration three different contributions for transport of LDL: normal and leaky junctions of endothelial cells, as well as their vesicular pathway. A comprehensive mathematical model, which is based on solving the set of PDEs for conservation of mass, momentum, and concentration, is completed by introducing the wall shear-stress (WSS) dependent transport properties of the arterial wall. Several variants of the model are evaluated, including the constant and wall shear-stress dependent transport properties of the endothelium, as well as different representation of the arterial wall internal structure. The response of the model on changing the transmural pressure (to simulate hypertension effects) and geometrical shapes of the stenosis (to mimic the various stages of atherosclerosis development) is also presented. It is shown that the present model can predict the levels of LDL inside the arterial wall in good agreement with experimental studies in pressurized rabbit aorta under similar conditions. The model is recommended for future simulations of LDL accumulation in the patient-specific cardiovascular system conditions.@en