Assessing the Spatiotemporal Characteristics, Factor Importance, and Health Impacts of Air Pollution in Seoul by Integrating Machine Learning into Land-Use Regression Modeling at High Spatiotemporal Resolutions.

Previous studies have characterized spatial patterns of air pollution with land-use regression (LUR) models. However, the spatiotemporal characteristics of air pollution, the contribution of various factors to them, and the resultant health impacts have yet to be evaluated comprehensively. This study integrates machine learning (random forest) into LUR modeling (LURF) with intensive evaluations to develop high spatiotemporal resolution prediction models to estimate daily and diurnal PM2.5 and NO2 in Seoul, South Korea, at the spatial resolution of 500 m for a year (2019) and to then evaluate the contribution of driving factors and quantify the resultant premature mortality. Our results show that incorporating the random forest algorithm into our LUR model improves the model performance. Meteorological conditions have a great influence on daily models, while land-use factors play important roles in diurnal models. Our health assessment using dynamic population data estimates that PM2.5 and NO2 pollution, when combined, causes a total of 11,183 (95% CI: 5837-16,354) premature mortalities in Seoul in 2019, of which 64.9% are due to PM2.5, while the remaining are attributable to NO2. The air pollution-attributable health impacts in Seoul are largely caused by cardiovascular diseases including stroke. This study pinpoints the significant spatiotemporal variations and health impact of PM2.5 and NO2 in Seoul, providing essential data for epidemiological research and air quality management.

Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution.

Air pollution has been recognized as a global issue, through adverse effects on environment and health. While vertical atmospheric processes substantially affect urban air pollution, traditional epidemiological research using Land-Use Regression (LUR) modeling usually focused on ground-level attributes without considering upper-level atmospheric conditions. This study aimed to integrate Doppler LiDAR and machine learning techniques into LUR models (LURF-LiDAR) to comprehensively evaluate urban air pollution in Hong Kong, and to assess the complex interactions between vertical atmospheric processes and urban air pollution from long-term (i.e., annual) and short-term (i.e., two air pollution episodes) views in 2021. The results demonstrated significant improvements in model performance, achieving a CV R2 of 0.81 (95 % CI: 0.75-0.86) for the long-term PM2.5 prediction and 0.90 (95 % CI: 0.87-0.91) for a short-term one. Approximately 69 % of ground-level air pollution arose from the mixing of ground- and lower-level (105 m-225 m) particles, while 21 % was associated with upper-level (825 m-945 m) atmospheric processes. The identified transboundary air pollution (TAP) layer was located at ~900 m above the ground. The identified Episode one (E1: 7 Jan-22 Jan) was induced by the accumulation of local emissions under stable atmospheric conditions, whereas Episode two (E2: 13 Dec-24 Dec) was regulated by TAP under instable and turbulent conditions. Our improved air quality model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.

Unit-related factors that affect nursing time with patients: spatial analysis of the time and motion study.

OBJECTIVE: The primary goal of this study was to test the hypothesis that nurses adopt distinct movement strategies based on features of unit topology and nurse assignments. The secondary goal was to identify aspects of unit layout or organization that influence the amount of time nurses spend in the patient room. BACKGROUND: Previous research has demonstrated a link between nursing hours and patient outcomes. Unit layout may affect direct patient care time by determining aspects of nurse behavior, such as the amount of time nurses spend walking. The recent nurses' Time and Motion study employed multiple technologies to track the movements and activities of 767 medical-surgical nurses. With regard to unit layout, initial analysis of the data set did not detect differences between types of units and time spent in the patient room. The analysis reported here applies novel techniques to this data set to examine the relationship between unit layout and nurse behavior. METHODS: Techniques of spatial analysis, borrowed from the architectural theory of spatial syntax, were applied to the Time and Motion data set. Motion data from radio-frequency identification tracking of nurses was combined with architectural drawings of the study units and clinical information such as nurse-patient assignment. Spatial analytic techniques were used to determine the average integration or centrality of nurse assignments for each shift. RESULTS: Nurse assignments with greater average centrality to all assigned rooms were associated with a higher number of entries to patient rooms, as well as to the nurse station. Number of entries to patient rooms was negatively correlated with average time per visit, but positively correlated with total time spent in patient rooms. The data describe two overall strategies of nurse mobility patterns: fewer, longer visits versus more frequent, shorter visits. CONCLUSIONS: Results suggest that the spatial qualities of nurse assignments and unit layout affect nurse strategies for moving through units and affect how frequently nurses enter patient rooms and the nurse station.