Suitable temperature indicator for adverse health impacts in sub-tropical cities: a case study in Hong Kong from 2010-2019.

Heat-health warning systems and services are important preventive actions for extreme heat, however, global evidence differs on which temperature indicator is more informative for heat-health outcomes. We comprehensively assessed temperature predictors on their summer associations with adverse health impacts in a high-density subtropical city. Maximum, mean, and minimum temperatures were examined on their associations with non-cancer mortality and hospital admissions in Hong Kong during summer seasons 2010-2019 using Generalized Additive Models and Distributed Lag Non-linear Models. In summary, mean and minimum temperatures were identified as strong indicators for mortality, with a relative risk(RR) and 95% confidence interval(CI) of 1.037 (1.006-1.069) and 1.055 (1.019-1.092), respectively, at 95th percentile vs. optimal temperature. Additionally, minimum temperatures captured the effects of hospital admissions, RR1.009 (95%CI: 1.000- 1.018). In stratified analyses, significant associations were found for older adults, female sex, and respiratory-related outcomes. For comparison, there was no association between maximum temperature and health outcomes. With climate change and projected increase of night-time warming, the findings from this comprehensive assessment method are useful to strengthen heat prevention strategies and enhance heat-health warning systems. Other locations could refer to this comprehensive method to evaluate their heat risk, especially in highly urbanized environments and subtropical cities.

Interpretable semi-supervised clustering enables universal detection and intensity assessment of diverse aviation hazardous winds.

The identification of aviation hazardous winds is crucial and challenging in air traffic management for assuring flight safety, particularly during the take-off and landing phases. Existing criteria are typically tailored for special wind types, and whether there exists a universal feature that can effectively detect diverse types of hazardous winds from radar/lidar observations remains as an open question. Here we propose an interpretable semi-supervised clustering paradigm to solve this problem, where the prior knowledge and probabilistic models of winds are integrated to overcome the bottleneck of scarce labels (pilot reports). Based on this paradigm, a set of high-dimensional hazard features is constructed to effectively identify the occurrence of diverse hazardous winds and assess the intensity metrics. Verification of the paradigm across various scenarios has highlighted its high adaptability to diverse input data and good generalizability to diverse geographical and climate zones.

AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports.

The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017-2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R.

Interpretable ensemble imbalance learning strategies for the risk assessment of severe-low-level wind shear based on LiDAR and PIREPs.

The occurrence of severe low-level wind shear (S-LLWS) events in the vicinity of airport runways poses a significant threat to flight safety and exacerbates a burgeoning problem in civil aviation. Identifying the risk factors that contribute to occurrences of S-LLWS can facilitate the improvement of aviation safety. Despite the significant influence of S-LLWS on aviation safety, its occurrence is relatively infrequent in comparison to non-SLLWS incidents. In this study, we develop an S-LLWS risk prediction model through the utilization of ensemble imbalance learning (EIL) strategies, namely, BalanceCascade, EasyEnsemble, and RUSBoost. The data for this study were obtained from PIREPs and LiDAR at Hong Kong International Airport. The analysis revealed that the BalanceCascade strategy outperforms EasyEnsemble and RUSBoost in terms of prediction performance. Afterward, the SHapley Additive exPlanations (SHAP) interpretation tool was used in conjunction with the BalanceCascade model for the risk assessment of various factors. The four most influential risk factors, according to the SHAP interpretation tool, were hourly temperature, runway 25LD, runway 25LA, and RWY (encounter location of LLWS). S-LLWS was likely to happen at Runway 25LD and Runway 25LA in temperatures ranging from low to moderate. Similarly, a high proportion of S-LLWS events occurred near the runway threshold, and a relatively small proportion occurred away from it. The EIL strategies in conjunction with the SHAP interpretation tool may accurately predict the S-LLWS without the need for data augmentation in the data pre-processing phase.

Numerical simulation research on the overturning of gantry crane by downbursts.

Based on the simulation of the fluid-structure interaction response, the cause of an overturning of a gantry crane induced by a downburst in Shenzhen is studied in this paper. According to the results, (1) Vicroy's downburst model could establish the steady-state wind field of the downburst more reasonably when there was only low-level wind speed observation data, and its simulation results were close to the two-dimensional downburst numerical simulation results; (2) Compared with the normal exponential vertical profile of wind speed, the disturbance caused by the front girder of the double-girder gantry crane structure under the downburst wind field was more severe, which increases the probability of the gantry crane overturning. (3) The downwind displacement of the main girder of the gantry crane under the condition of downburst is far greater than that under the normal condition. At the same time, under the condition of downburst, the pressure difference on the surface of the gantry crane was greater, and the distribution of the support reaction force was more uneven, resulting in a stronger overturning tendency of the gantry crane. (4) Under the condition of downburst, the overturning moment and the shearing force borne by the foundation of gantry crane exceeded the critical value to maintain the stability of the gantry crane by the gravity, resulting in the overturning of the gantry crane.

Assessing wind field characteristics along the airport runway glide slope: an explainable boosting machine-assisted wind tunnel study.

Aircraft landings are especially perilous when the wind is gusty near airport runways. For this reason, an aircraft may deviate from its glide slope, miss its approach, or even crash in the worst cases. In the study, we used the state-of-the-art glass-box model, the Explainable Boosting Machine (EBM), to estimate the variation in headwind speed and turbulence intensity along the airport runway glide slope and to interpret the various contributing factors. To begin, the wind field characteristics were examined by developing a scaled-down model of Hong Kong International Airport (HKIA) runway as well as and the surrounding buildings and complex terrain in the TJ-3 atmospheric boundary layer wind tunnel. The placement of probes along the glide slope of the model runway aided in the measurement of wind field characteristics at different locations in the presence and absence of surrounding buildings. Next, the experimental data was used to train the EBM model in conjunction with Bayesian optimization approach. The counterpart black box models (extreme gradient boosting, random forest, extra tree and adaptive boosting) as well as other glass box models (linear regression and decision tree) were compared with the outcomes of the EBM model. Based on the holdout testing data, the EBM model revealed superior performance for both variation in headwind speed and turbulence intensity in terms of mean absolute error, mean squared error, root mean squared error and R-square values. To further evaluate the impact of different factors on the wind field characteristics along the airport runway glide slope, the EBM model allows for a full interpretation of the contribution of individual and pairwise interactions of factors to the prediction results from both a global and a local perspective.

Short-Term Nationwide Airport Throughput Prediction With Graph Attention Recurrent Neural Network.

With the dynamic air traffic demand and the constrained capacity resources, accurately predicting airport throughput is essential to ensure the efficiency and resilience of air traffic operations. Many research efforts have been made to predict traffic throughputs or flight delays at an airport or over a network. However, it is still a challenging problem due to the complex spatiotemporal dynamics of the highly interacted air transportation systems. To address this challenge, we propose a novel deep learning model, graph attention neural network stacking with a Long short-term memory unit (GAT-LSTM), to predict the short-term airport throughput over a national air traffic network. LSTM layers are included to extract the temporal correlations in the data, while the graph attention mechanism is used to capture the spatial dependencies. For the graph attention mechanism, two graph modeling methods, airport-based graph and OD-pair graph are explored in this study. We tested the proposed model using real-world air traffic data involving 65 major airports in China over 3 months in 2017 and compared its performance with other state-of-the-art models. Results showed that the temporal pattern was the dominate factor, compared to the spatial pattern, in predicting airport throughputs over an air traffic network. Among the prediction models that we compared, both the proposed model and LSTM performed well on prediction accuracy over the entire network. Better performance of the proposed model was observed when focusing on airports with larger throughputs. We also conducted an analysis on model interpretability. We found that spatiotemporal correlations in the data were learned and shown via the model parameters, which helped us to gain insights into the topology and the dynamics of the air traffic network.

Intercomparison of Local Warming Trends of Shanghai and Hong Kong Based on 120-Year Temperature Observational Data.

Using surface air temperature observations from 1901 to 2020, this study compared the warming trends of Shanghai and Hong Kong over a period of 120 years. The statistical results reveal the following: (1) The average temperatures of the two cities underwent fluctuating increases during the past 120 years, with linear warming rates of 0.23 °C/decade in Shanghai and 0.13 °C/decade in Hong Kong. (2) The fluctuation ranges of maximum temperature in the two cities were considerably higher than those of mean temperature. Moreover, in both cities, the annual mean maximum temperature decreased during a phase of more than a decade. The fluctuation ranges of minimum temperature were smaller, whereas the linear increases were higher than those for the mean temperature. (3) The diurnal temperature ranges (DTRs) of the two cities decreased; a certain phase of the decreases in DTR in the two cities was caused by decreases in the maximum temperature. (4) At a certain stage of urban development, owing to the shading effect of new high-rise buildings, the solar shortwave radiation reaching the Earth's surface decreased, and anthropogenic heat generated by the energy consumption of buildings and urban human activities at that time was not sufficient to make up for the reduced shortwave radiation. This result may have led to the declines in the maximum temperature experienced by both cities. (5) Currently, the number of hot days and extremely hot days in the two cities has increased significantly compared with that a century ago, indicating that climate warming has an adverse impact on human settlements.

Probable cross-corridor transmission of SARS-CoV-2 due to cross airflows and its control.

A COVID-19 outbreak occurred in May 2020 in a public housing building in Hong Kong - Luk Chuen House, located in Lek Yuen Estate. The horizontal cluster linked to the index case' flat (flat 812) remains to be explained. Computational fluid dynamics simulations were conducted to obtain the wind-pressure coefficients of each external opening on the eighth floor of the building. The data were then used in a multi-zone airflow model to estimate the airflow rate and aerosol concentration in the flats and corridors on that floor. Apart from flat 812 and corridors, the virus-laden aerosol concentrations in flats 811, 813, 815, 817 and 819 (opposite to flat 812, across the corridor) were the highest on the eighth floor. When the doors of flats 813 and 817 were opened by 20%, the hourly-averaged aerosol concentrations in these two flats were at least four times as high as those in flats 811, 815 and 819 during the index case's home hours or the suspected exposure period of secondary cases. Thus, the flats across the corridor that were immediately downstream from flat 812 were at the highest exposure risk under a prevailing easterly wind, especially when their doors or windows that connected to the corridor were open. Given that the floorplan and dimension of Luk Chuen House are similar to those of many hotels, our findings provide a probable explanation for COVID-19 outbreaks in quarantine hotels. Positive pressure and sufficient ventilation in the corridor would help to minimise such cross-corridor infections.

Impact of the COVID-19 on the vertical distributions of major pollutants from a tower in the Pearl River Delta.

The outbreak of the 2019 novel coronavirus (COVID-19) had a large impact on human health and socio-economics worldwide. The lockdown implemented in China beginning from January 23, 2020 led to sharp reductions in human activities and associated emissions. The declines in primary pollution provided a unique opportunity to examine the relationship between anthropogenic emissions and air quality. This study reports on air pollutant and meteorological measurements at different heights from a tall tower in the Pearl River Delta. These measurements were used to investigate the vertical scale response of pollutants to understand reductions in human activities. Compared to that in the pre-lockdown period (from December 16, 2019), the concentrations of surface layer nitric oxide (NOx), fine particulate matter (PM2.5), and daily maximum 8 h average ozone (MDA8O3) declined significantly during the lockdown by 76.8%, 49.4%, and 18.6%, respectively. Although the vertical profiles of NOx and O3 changed during the lockdown period, those of PM2.5 remained the same. During the lockdown period, there were statistically significant correlations between PM2.5 and O3 but not between PM2.5 and NOx at four heights, indicating that the main composition of PM2.5 have dramatically changed, during which the impact of NOx on PM2.5 became insignificant. Additionally, O3 concentrations were also insensitive to NOx concentrations during the lockdown, implying that O3 levels were more of a representative of regional background level. In this case, local photochemical formation is no longer a significant ozone source. This evidence suggests that it is possible to mitigation of PM2.5 and O3 levels simultaneously by significant reductions in anthropogenic emissions.

On the Use of Dynamic Calibration to Correct Drop Counter Rain Gauge Measurements.

Dynamic calibration was performed in the laboratory on two catching-type drop counter rain gauges manufactured as high-sensitivity and fast response instruments by Ogawa Seiki Co. Ltd. (Japan) and the Chilbolton Rutherford Appleton Laboratory (UK). Adjustment procedures were developed to meet the recommendations of the World Meteorological Organization (WMO) for rainfall intensity measurements at the one-minute time resolution. A dynamic calibration curve was derived for each instrument to provide the drop volume variation as a function of the measured drop releasing frequency. The trueness of measurements was improved using a post-processing adjustment algorithm and made compatible with the WMO recommended maximum admissible error. The impact of dynamic calibration on the rainfall amount measured in the field at the annual and the event scale was calculated for instruments operating at two experimental sites. The rainfall climatology at the site is found to be crucial in determining the magnitude of the measurement bias, with a predominant overestimation at the low to intermediate rainfall intensity range.

Dual challenges of heat wave and protective facemask-induced thermal stress in Hong Kong.

During the COVID-19 pandemic, wearing protective facemasks (PFMs) can effectively reduce infection risk, but the use of PFMs can amplify heat-related health risks. We studied the amplified PFM-induced human thermal stress via both field measurements and model simulations over a typical subtropical mountainous city, Hong Kong. First, a hot and humid PFM microenvironment has been observed with high temperature (34-35 °C) and high humidity (80-95%), resulting in an aggravated facial thermal stress with a maximal PFM-covered facial heat flux of 500 W/m2 under high-intensity activities. Second, to predict the overall PFM-inclusive human thermal stress, we developed a new facial thermal load model, S PFM and a new human-environment adaptive thermal stress (HEATS) model by coupling S PFM with an enhanced thermal comfort model to resolve modified human-environment interactions with the intervention of PFM under realistic climatic and topographical conditions. The model was then applied to predict spatiotemporal variations of PFM-inclusive physiological subjective temperature (PST) and corresponding heat stress levels during a typical heat wave event. It was found wearing PFM can significantly aggravate human thermal stress over Hong Kong with a spatially averaged PST increment of 5.0 °C and an additional spatial area of 158.4% exposed to the severest heat risks. Besides, PFM-inclusive PST was found to increase nonlinearly with terrain slopes at a rate of 1.3-3.9 °C/10°(slope), owing to elevated metabolic heat production. Furthermore, urban residents were found to have higher PFM-aggravated heat risks than rural residents, especially at night due to synergistic urban heat and moisture island effects.

Understanding heat patterns produced by vehicular flows in urban areas.

Vehicular traffic has strong implication in the severity and degree of Urban Heat Island (UHI) effect in a city. It is crucial to map and monitor the spatio-temporal heat patterns from vehicular traffic in a city. Data observed from traffic counting stations are readily available for mapping the traffic-related heat across the stations. However, macroscopic models utilizing traffic counting data to estimate dynamic directional vehicular flows are rarely established. Our work proposes a simple and robust cell-transmission-model to simulate all the possible cell-based origin-destination trajectories of vehicular flows over time, based on the traffic counting stations. Result shows that the heat patterns have notable daily and weekly periodical circulation/pattern, and volumes of heat vary significantly in different grid cells. The findings suggest that vehicular flows in some places are the dominating influential factor that make the UHI phenomenon more remarkable.

Process analysis of a regional air pollution episode over Pearl River Delta region, China, using the MM5-CMAQ model.

UNLABELLED: This study focuses on the influences of a warm high-pressure meteorological system on aerosol pollutants, employing the simulations by the Models-3/CMAQ system and the observations collected during October 10-12, 2004, over the Pearl River Delta (PRD) region. The results show that the spatial distributions of air pollutants are generally circular near Guangzhou and Foshan, which are cities with high emissions rates. The primary pollutant is particulate matter (PM) over the PRD. MM5 shows reasonable performance for major meteorological variables (i.e., temperature, relative humidity, wind direction) with normalized mean biases (NMB) of 4.5-38.8% and for their time series. CMAQ can capture one peak of all air pollutant concentrations on October 11, but misses other peaks. The CMAQ model systematically underpredicts the mass concentrations of all air pollutants. Compared with chemical observations, SO2 and O3 are predicted well with a correlation coefficient of 0.70 and 0.65. PM2.5 and NO are significantly underpredicted with an NMB of 43% and 90%, respectively. The process analysis results show that the emission, dry deposition, horizontal transport, and vertical transport are four main processes affecting air pollutants. The contributions of each physical process are different for the various pollutants. The most important process for PM10 is dry deposition, and for NO(x) it is transport. The contributions of horizontal and vertical transport processes vary during the period, but these two processes mostly contribute to the removal of air pollutants at Guangzhou city, whose emissions are high. For this high-pressure case, the contributions of the various processes show high correlations in cities with the similar geographical attributes. According to the statistical results, cities in the PRD region are divided into four groups with different features. The contributions from local and nonlocal emission sources are discussed in different groups. IMPLICATIONS: The characteristics of aerosol pollution episodes are intensively studied in this work using the high-resolution modeling system MM5/SMOKE/CMAQ, with special efforts on examining the contributions of different physical and chemical processes to air concentrations for each city over the PRD region by a process analysis method, so as to provide a scientific basis for understanding the formation mechanism of regional aerosol pollution under the high-pressure system over PRD.

Accurate extraction of Lagrangian coherent structures over finite domains with application to flight data analysis over Hong Kong International Airport.

Locating Lagrangian coherent structures (LCS) for dynamical systems defined on a spatially limited domain present a challenge because trajectory integration must be stopped at the boundary for lack of further velocity data. This effectively turns the domain boundary into an attractor, introduces edge effects resulting in spurious ridges in the associated finite-time Lyapunov exponent (FTLE) field, and causes some of the real ridges of the FTLE field to be suppressed by strong spurious ridges. To address these issues, we develop a finite-domain FTLE method that renders LCS with an accuracy and fidelity that is suitable for automated feature detection. We show the application of this technique to the analysis of velocity data from aircraft landing at the Hong Kong International Airport.