Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics.

Environmental deprivation is an issue influencing the urban wellbeing of a city. However, there are limitations to spatiotemporally monitoring the environmental deprivation. Thus, recent studies have introduced the concept of “Smart City” with the use of advanced technology for real-time environmental monitoring. In this regard, this study presents an improved Integrated Environmental Monitoring System (IIEMS) with the consideration on nine environmental parameters: temperature, relative humidity, PM_2.5, PM₁₀, CO, SO₂, volatile organic compounds (VOCs), UV index, and noise. This system was comprised of a mobile unit and a server-based platform with nine highly accurate micro-sensors in-coupling into the mobile unit for estimating these environmental exposures. A calibration test using existing monitoring station data was conducted in order to evaluate the systematic errors. Two applications with the use of the new system were also conducted under different scenarios: pre- and post-typhoon days and in areas with higher and lower vegetation coverage. Linear regressions were applied to predict the changes in environmental quality after a typhoon and to estimate the difference in environmental exposures between urban roads and green spaces. The results show that environmental exposures interact with each other, while some exposures are also controlled by location. PM_2.5 had the highest change after a typhoon with an estimated 8.0 μg/m³ decrease that was controlled by other environmental factors and geographical location. Sound level and temperature were significantly higher on urban roads than in urban parks. This study demonstrates the potential to use IIEMS for environmental quality measurements under the greater framework of a Smart City and for sustainability research.

Semantic segmentation-based methods have attracted extensive attention in oil spill detection from SAR images. However, the existing approaches require a large number of finely annotated segmentation samples in the training stage. To alleviate this issue, we propose a composite oil spill detection framework, SAM-OIL, comprising an object detector (e.g., YOLOv8), an Adapted Segment Anything Model (SAM), and an Ordered Mask Fusion (OMF) module. SAM-OIL is the first application of the powerful SAM in oil spill detection. Specifically, the SAM-OIL strategy uses YOLOv8 to obtain the categories and bounding boxes of oil spill-related objects, then inputs bounding boxes into the Adapted SAM to retrieve category-agnostic masks, and finally adopts the OMF module to fuse the masks and categories. The Adapted SAM, combining a frozen SAM with a learnable Adapter module, can enhance SAM’s ability to segment ambiguous objects. The OMF module, a parameter-free method, can effectively resolve pixel category conflicts within SAM. Experimental results demonstrate that SAM-OIL surpasses existing semantic segmentation-based oil spill detection methods, achieving mIoU of 69.52%. The results also indicated that both OMF and Adapter modules can effectively improve the accuracy in SAM-OIL.

Considering the surge in e-learning growth over the last decade and the proliferation of mobile devices in the Bring Your Own Device generation, this paper reviews selected use cases of Bluetooth beacons in educational situations. We review the contribution of Bluetooth beacons to a mixed pedagogy that uses digital and physical learning spaces, and we discuss the pilot deployment of Bluetooth beacons at the Hong Kong Polytechnic University, Hong Kong, to enhance physical learning spaces. Our work represents one of the first deployments of Bluetooth beacons in a university teaching and learning capacity and provides a starting point for others attempting to utilise beacon-enabled location-based services to enhance learner experiences. The widespread adoption of beacon technology in educational institutions has not yet occurred, and the most common usage of beacon transmitters and systems in education is for attendance-taking and dissemination of teaching material. Mobile applications are constantly being developed to utilise the location-based services provided by beacons to enhance student learning and effectiveness. This paper also discusses the sustainability benefits of beacon systems, especially in the contexts of smart campus and smart city development.

The massive lockdown of global cities during the COVID-19 pandemic is substantially improving the atmospheric environment, which for the first time, urban mobility is virtually reduced to zero, and it is then possible to establish a baseline for air quality. By comparing these values with pre-COVID-19 data, it is possible to infer the likely effect of urban mobility and spatial configuration on the air quality. In the present study, a time-series prediction model is enhanced to estimate the nationwide NO₂ concentrations before and during the lockdown measures in the United States, and 54 cities are included in the study. The prediction generates a notable NO₂ difference between the observations if the lockdown is not considered, and the changes in urban mobility can explain the difference. It is found that the changes in urban mobility associated with various road textures have a significant impact on NO₂ dispersion in different types of climates.

The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing rapidly. Susceptibility studies have been conducted in specific countries, where COVID-19 infection and mortality rates were highly related to demographics and air pollution, especially PM_2.5, but there are few studies on a global scale. This paper is an exploratory study of the relationship between confirmed COVID-19 cases and death toll per million population, population density, and PM_2.5 concentration on a worldwide basis. A multivariate linear regression based on Moran eigenvector spatial filtering model and Geographically weighted regression model were undertaken to analyze the relationship between population density, PM_2.5 concentration, and COVID-19 infection and mortality rate, and a geostatistical method with bivariate local spatial association analysis was adopted to explore their spatial correlations. The results show that there is a statistically significant positive relationship between COVID-19 confirmed cases and death toll per million population, population density, and PM_2.5 concentration, but the relationship displays obvious spatial heterogeneity. While some adjacent countries are likely to have similar characteristics, it suggests that the countries with close contacts/sharing borders and similar spatial pattern of population density and PM_2.5 concentration tend to have similar patterns of COVID-19 risk. The analysis provides an interpretation of the statistical and spatial association of COVID-19 with population density and PM_2.5 concentration, which has implications for the control and abatement of COVID-19 in terms of both infection and mortality.