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 PM_2.5 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 PM_2.5 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 PM_2.5, 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 PM_2.5 and NO₂ in Seoul, providing essential data for epidemiological research and air quality management.

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 PM_2.5 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 PM_2.5 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 PM_2.5, 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 PM_2.5 and NO₂ in Seoul, providing essential data for epidemiological research and air quality management.

The paper reports the energy modelling process of 11 university buildings with the use of a normative energy calculation method. The broad aim of this exercise is to model a set of buildings efficiently so as to capture heterogeneity across buildings and minimize auditing requirements. First, energy model inputs are scrutinized and improved to better represent the actual use of the buildings. The second set of model improvements aim to identify and test those parameters that can be uniformly described across all the buildings, thus reducing overall modelling effort. Using sensitivity analysis of parameters per building, we demonstrate the validity of assigning a common range of values to key input parameters across the building portfolio. Gas and electricity consumption are analyzed separately. Our results show that for electricity consumption, a deeper sub-categorization of activities within buildings is important. On the other hand, accuracy of gas consumption relies on parameters associated with the building fabric.