Estimation of On‐Road PM2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

How to reduce the health risks for commuters, caused by air pollution such as PM2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on‐road PM2.5 throughout the whole city. First, the micro‐scale PM2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat‐8 top‐of‐atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index‐based built‐up index, are incorporated within the TOA‐LUR modeling. On‐road PM2.5 distributions are then mapped in high‐spatial‐resolution. The maps obtained can be used to find healthy travel routes with less PM2.5. The proposed framework was applied in high‐density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA‐LUR Geographical and Temporal Weighted Regression models is at a high‐level with an R2 of 0.70–0.90. The newly introduced GVI index played an important role in these estimations. The PM2.5 distribution maps at high‐spatial‐resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high‐density cities. Plain Language Summary Every year, exposure to outdoor air pollution has caused 7 million premature deaths and resulted in the loss of millions more healthy years of life. Therefore, it's critical to establish planning tools to support people's healthier and cleaner daily travel. By land use regression (LUR) modeling enhanced by the Landsat‐8 top‐of‐atmosphere (TOA) data and microscale geographic predictors, the original framework proposed in this study provides an estimation and a mapping of on‐road PM2.5 at the fine scale, providing people with healthier travel choices. The estimation performance of the proposed seasonal TOA‐LUR Geographical and Temporal Weighted Regression models achieved a high R2 of 0.70–0.90. It may become a reference and guide for high‐density cities, for smart travel planning and refined monitoring of air pollution in urban areas. It is hoped, therefore, that this study assists more people in