A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

Diurnal dynamics of stress and mood during COVID-19 lockdown: a large multinational ecological momentary assessment study.

The COVID-19 pandemic resulted in severe disruption to people's lives as governments imposed national 'lockdowns'. Several large surveys have underlined the detrimental short- and long-term mental health consequences resulting from this disruption, but survey findings are only informative of individuals' retrospectively reported psychological states. Furthermore, knowledge on psychobiological responses to lockdown restrictions is scarce. We used smartphone-based real-time assessments in 731 participants for 7 days and investigated how individuals' self-reported stress and mood fluctuated diurnally during lockdown in spring 2020. We found that age, gender, financial security, depressive symptoms and trait loneliness modulated the diurnal dynamics of participants' momentary stress and mood. For example, younger and less financially secure individuals showed an attenuated decline in stress as the day progressed, and similarly, more lonely individuals showed a diminished increase in calmness throughout the day. Hair collected from a subsample (n = 140) indicated a decrease in cortisol concentrations following lockdown, but these changes were not related to any of the assessed person-related characteristics. Our findings provide novel insights into the psychobiological impact of lockdown and have implications for how, when and which individuals might benefit most from interventions during psychologically demanding periods.