Deep stratospheric intrusion and Russian wildfire induce enhanced tropospheric ozone pollution over the northern Tibetan Plateau

By using ozonesonde measurements during July–August in 2016, 2019, and 2020 at Golmud and Qaidam, European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis ERA5 data, satellite-borne Moderate Resolution Imaging Spectrometer data products, and backward trajectory calculations from the chemical Lagrangian model of the stratosphere (CLaMS) model, this study analyzes vertical ozone distributions and explores the influence of deep stratospheric intrusions and wildfires on ozone variation in the northern Tibetan Plateau (TP) during the Asian summer monsoon period. Large ozone partial pressures were observed between 20 and 30 km, with a maximum of ~16 mPa at approximately 27 km latitude. The comparisons between the vertical ozone profiles with and without the occurrence of stratospheric intrusions showed that their relative ozone difference was up to 72.4% in the tropopause layer (15.8 km), and a secondary maximum of 66.7% existed in the middle troposphere (10.1 km). The stratospheric intrusions dried the atmosphere by 52.9% and enhanced the ozone columns by 26.1% below the upper troposphere and lower stratosphere. A case study of deep stratospheric intrusion exhibited the occurrence of large ozone partial pressure in the middle troposphere in detail, with an ozone peak of ~6 mPa at 10 km, which was caused by a tropopause fold associated with the westerly wind jet at the north flank of the Asian summer monsoon anticyclone. The stratospheric intrusion processes effectively transported the cold and dry air mass with high ozone in the stratosphere downward to the middle troposphere over the northern TP. This study also confirmed that by long-range transport processes, large wildfire smoke occurred around central and eastern Russia on 19–26 July 2016 greatly caused ozone pollution in the troposphere (6 km depth from the surface) over the northern TP

Wearable Travel Aid for Environment Perception and Navigation of Visually Impaired People

Assistive devices for visually impaired people (VIP) which support daily traveling and improve social inclusion are developing fast. Most of them try to solve the problem of navigation or obstacle avoidance, and other works focus on helping VIP to recognize their surrounding objects. However, very few of them couple both capabilities (i.e., navigation and recognition). Aiming at the above needs, this paper presents a wearable assistive device that allows VIP to (i) navigate safely and quickly in unfamiliar environment, and (ii) to recognize the objects in both indoor and outdoor environments. The device consists of a consumer Red, Green, Blue and Depth (RGB-D) camera and an Inertial Measurement Unit (IMU), which are mounted on a pair of eyeglasses, and a smartphone. The device leverages the ground height continuity among adjacent image frames to segment the ground accurately and rapidly, and then search the moving direction according to the ground. A lightweight Convolutional Neural Network (CNN)-based object recognition system is developed and deployed on the smartphone to increase the perception ability of VIP and promote the navigation system. It can provide the semantic information of surroundings, such as the categories, locations, and orientations of objects. Human–machine interaction is performed through audio module (a beeping sound for obstacle alert, speech recognition for understanding the user commands, and speech synthesis for expressing semantic information of surroundings). We evaluated the performance of the proposed system through many experiments conducted in both indoor and outdoor scenarios, demonstrating the efficiency and safety of the proposed assistive system