Estimation of On‐Road PM(2.5) 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 PM(2.5) 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 PM(2.5) throughout the whole city. First, the micro‐scale PM(2.5) 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 PM(2.5) distributions are then mapped in high‐spatial‐resolution. The maps obtained can be used to find healthy travel routes with less PM(2.5). 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 R (2) of 0.70–0.90. The newly introduced GVI index played an important role in these estimations. The PM(2.5) 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.