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.

A novel approach for assessing the spatiotemporal trend of health risk from ambient particulate matter components: Case of Hong Kong.

The spatiotemporal assessment of health risk due to exposure to particulate matter (PM) components should be well studied because of the different toxicity among PM components. However, this research topic has long been overlooked. This study aimed to examine the spatiotemporal variability in ambient respirable PM (PM10) components associated inhalation carcinogenic and non-carcinogenic risk (ICR and INCR) in Hong Kong over 2015-2019. The land-use regression (LUR) approach was adopted to predict the spatial distribution of PM10 component concentrations for the period of 2015-2019, whereas the ICR and INCR values of PM10 components were also estimated using the classic health risk assessment method. Both concentration of PM10 and INCR of PM10 components showed a general decreasing trend, while ICR of PM10 components increased slightly over the study period. LUR-model-based spatial maps at 500 m × 500 m resolution revealed the important spatial variability in PM10 and its eleven components, and their associated ICR and INCR values. High pollution levels and high ICR and INCR of studied PM10 components were generally found in developed urban areas and along the road network. Despite the fact that the PM10 concentrations met the Hong Kong annual PM10 air quality objective of 50 µg/m3, there was still significant potential health risk from the studied PM10 components. This study highlights the importance of taking PM component concentrations and associated inhalation health risk as well as PM mass concentrations into account for the perspective of air quality management and protecting public health.

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 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 CV R2 values of 0.81 (95 % CI: 0.75-0.86) for the long-term PM2.5 prediction model and 0.90 (95 % CI: 0.87-0.91) for the short-term models. 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 prediction model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.