Field Evaluation of Four Low-cost PM Sensors and Design, Development and Field Evaluation of A Wearable PM Exposure Monitoring System

To mitigate the significant biases/errors in research studying the associations between PM and health, which are introduced by the coarse/inadequate assessments of PM exposure from conventional PM monitoring paradigm, a personalized monitoring system consisting of a low-cost wearable PM device is proposed. However, due to the absence of a unifying evaluation protocol for low-cost PM sensors, the evaluation results/performance specifications from existing studies/datasheets are of limited reference values when attempting to determine the best candidate for the proposed system. In this regard, the authors appeal to the research community to develop a standardized evaluation protocol for low-cost PM sensors/devices, and a unifying attempt is established in this manuscript by adopting the definitive terminology from international documents and the evaluation metrics regarded as best practices. Collocated on the rooftop of the HKUST Supersite, four empirically selected PM sensors were compared against each other and calibrated against two reference monitors. They were then evaluated against the reference following the protocol. The PlanTower PMS-A003 sensor was selected for the wearable device as it outperformed the others in terms of affordability, portability, detection capability, data quality, as well as humidity and condensation insusceptibility. An automated approach was proposed to identify and remove the condensation associated abnormal measurements. The proposed device has better affordability and portability as well as similar usability and data accessibility compared to those existing devices recognized. The first 10 devices were also evaluated and calibrated at the Supersite. Additional 120 units were manufactured and delivered to the subjects to acquire their daily PM2.5 exposures for investigating the association with subclinical atherosclerosis

Improving Lidar Windshear Detection Efficiency by Removal of “Gentle Ramps”

The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called “gentle ramps”) which may nonetheless exceed the well-established alerting threshold. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). The “gentle ramp removal” (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. In all 5 runway corridors this is achieved with a proportionately smaller decrease—even with no changes in 2 cases—in the hit rate, highlighting the efficiency of the GRR function. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world

Application of spectral decomposition of LIDAR-based headwind profiles in windshear detection at the Hong Kong International Airport

In aviation, rapidly fluctuating headwind/tailwind may lead to high horizontal windshear, posing potential safety hazards to aircraft. So far, windshear alerts are issued by considering directly the headwind differences measured along the aircraft flight path (e.g. based on Doppler velocities from remote-sensing). In this paper, we propose and demonstrate a new methodology for windshear alerting with the technique of spectral decomposition. Through Fourier transformation of the LIDAR-based headwind profiles in 2012 and 2014 at arrival corridors 07LA and 25RA of the Hong Kong International Airport (HKIA), we study the occurrence of windshear in the spectral domain. Using a threshold-based approach, we investigate performance of single and multiple channel detection algorithms and validate the results against pilot reports. With the receiver operating characteristic (ROC) diagram, we successfully demonstrate feasibility of this approach to alert windshear by showing a comparable performance of the triple channel detection algorithm and a consistent hit rate gain (07LA in particular) of 4.5 to 8 % in quadruple channel detection against GLYGA, which is the currently operational algorithm in HKIA. We also observe that some length scales are particularly sensitive to windshear events which may be closely related to the local geography of HKIA. This study serves to open a new door for the methodology of windshear detection in the spectral domain for the aviation community

Aircraft Wake Recognition and Strength Classification Based on Deep Learning

Aircraft wake is a pair of counter-rotating vortices generated behind the aircraft, which can greatly impact the safety of fast takeoff and landing of aircraft and limit the improvement of airport capacity. The current wake parameter retrieval methods cannot locate the wake vortex's position and estimate its strength level in real time. To deal with this issue, a novel algorithm based on the YOLOv5s deep learning network is proposed. The new algorithm establishes a single vortex locating concept to adapt the wake vortex's evolution at complicate background wind field conditions, and proposes strength-based classification standard which can represent the real-time hazard of wake vortex to shorten the takeoff and landing intervals. Meanwhile, the EIOU loss function is introduced to improve the precision of YOLOv5s network. Compared with the state-of-the-art object detection approaches, such as Cascade R-CNN, FCOS, and YOLOv5l, the superiority of new method is demonstrated in terms of accuracy and robustness by using the field detection data from Hong Kong International Airport

A Spatio-Temporal Neural Network for Fine-Scale Wind Field Nowcasting Based on Lidar Observation

Fine-scale wind field nowcasting is of great significance in air traffic management, power grid operation, and so on. In this article, an indirect wind field nowcasting scheme based on lidar observation is presented, which contains an encoder-forecaster network based on the convolutional long short-term memory with balanced structure and a mask branch. The proposed nowcasting network is trained and evaluated based on the lidar observations throughout 2020 at Hong Kong International Airport. Comprehensive comparison with nine methods including the widely used optical flow technique and classic neural network show the good performance of the new network. It can capture the spatio-temporal features in the lidar observations and obtain better nowcasting results up to 27 min with a resolution of 100 m. The nowcasting errors are smaller than the retrieval errors reported in recent literature, demonstrating that the lidar observation nowcasting based on the new network can get fine-scale wind field nowcasting results with high efficiency

Treatment received by cancer patients.

<p>Treatment received by cancer patients.</p

Kaplan-Meier estimate of percentage of cardiovascular mortality.

<p>Kaplan-Meier estimate of percentage of cardiovascular mortality.</p

Multi-variate Cox regression model of recurrent stroke in patients with and without cancer.

<p>Abbreviations: HR = hazards ratio; CI = confidence interval.</p

Multi-variate Cox regression model of cardiovascular mortality in patients with and without cancer.

<p>Abbreviations: HR = hazards ratio; CI = confidence interval.</p