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.

This study analyzes seasonal characteristics and long-term variations in aerosol optical parameters in Hong Kong from 2006 to 2021 using AERONET data and satellite-based observations based on the extreme-point symmetric mode decomposition (ESMD) model. The dominant aerosol types in Hong Kong are mixed aerosols and urban/industrial aerosols with fine-mode sizes, and slightly absorbing or non-absorbing properties. Aerosol optical depth (AOD), Angstrom exponent (AE) and single scattering albedo (SSA) varied seasonally with a lower AOD but higher AE and SSA in summer, and elevated AOD but lower AE and SSA in spring and winter. The long-term variations show the year 2012 to be a turning point, with an upward trend in AOD and AE before 2012 and then downwards after 2012. However, for SSA, a rising trend was exhibited in both pre- and post-2012 periods, but with a larger gradient in the first period. The ESMD analysis shows shorter-term, non-linear fluctuations in aerosol optical parameters, with alternating increasing and declining trends. The examination of the relationships between AOD and meteorological factors based on the extreme gradient boosting (XGBoost) method shows that the effects of weather conditions on AOD are complex and non-monotonic. A lower relative humidity, higher wind speed in southwest directions and lower temperature are beneficial to the abatement of aerosol loads in Hong Kong. In conclusion, the findings of this study enhance the understanding of aerosol properties and the interactions between aerosol loading and meteorological factors.

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.

BACKGROUND Globally, more than 300 million people suffer from depressive disorders. Despite a wide range of conducted research, information on the mobility and daily routines of persons with depression remains lacking. OBJECTIVE To describe a preliminary explication of the association between mobility, well-being, and community participation of people with depression as they occur in real-time settings. METHODS We used a multivariate, time-series, single-subject, repeated measures (MRSRM) design. People with depression (N=33) were invited to use a GPS device for over two weeks for continuous mobility tracking, while information on their well-being was collected with paper surveys before and after two-weeks periods. The tracking records from 32 participants were imported into Geographical Information Systems (GIS) to extract the specific mobility information. Socio-demographic and environmental information (building density, greenness index and PM2.5) was derived for statistical analysis to explore the relationship between mobility, community participation and mental health using correlation coefficients and logistic regression analysis. RESULTS The more hours participants stayed at home, the more often they had depressive symptoms (r=0.47, P<.01) but their satisfaction with life was higher (r=0.38, P<.05). On the contrary, participants with longer total distance reported more stress (r=0.47, P<.01) and the more destinations participants travelled to, the more depressive symptoms they had (r=-0.36, P<.05). Wide travelling area was positively associated with stress and negatively associated with satisfaction with life, while more time spent at home was positively associated with depressive symptoms and low self-esteem. Time spent outside the home was also negatively associated with satisfaction with life and self-esteem. The great majority (91%) perceived the tracking device as comfortable and convenient and 31% had positive feelings about mobility tracking. Still, three participants (9%) expressed that the device caused inconvenience, stress or fair as the battery needed to be charged frequently. CONCLUSIONS Our study showed that having signs of depression is associated with mobility area and time spent at home among persons with depression, which may mean that their activity level in society is limited. Staying home may be a solution for avoiding extra stress, problems with self-esteem and maintaining satisfaction with life. Low-threshold access to health services should be tailored for each person based on their needs. More information based on studies using robust design, bigger sample size, and more accurate measurement methods, however, is required before individualized services can be fully achieved. Although using GPS may be a feasible data collection method, more effort should be put into collecting the data with easy-to-use technology to avoid extra stress in the participants.