Towards space-time modelling of PM_2.5 inhalation volume with ST-exposure

Air quality (AQ) is directly relevant with people's health while implementing effective methods for acquiring pollution details and assessing health impact are very important for public health management. In this paper, we design an end-to-end space-time modelling framework to estimate pixelwise PM_2.5 inhalation volume, called ST-Exposure which goes over the model's practicality and benefits on the following aspects: (1) Use a combination of fixed and mobile AQ sensors, we estimate PM_2.5 inhalation volume based on the inference of PM_2.5 exposure in Beijing (3025 km², 19 Jun − 16 Jul 2018) with the space-time resolution of 1 km × 1 km and 1 h, with <15 % SMAPE (%). (2) Achieve pixelwise PM_2.5 inhalation volume to be inferred with high-resolution (1 km × 1 km, hourly) at city scale, even with sparse space-time coverage. (3) Propose a new calculation mechanism of population distribution which is better than the traditional census-based method, and can achieve more reliable estimation of the total PM_2.5 inhalation volume over the whole region.