Song Jun
Department of Geography, Hong Kong Baptist University, Hong Kong.
Sci Total Environ. 2024 Oct 20;948:174888. doi: 10.1016/j.scitotenv.2024.174888. Epub 2024 Jul 18.
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 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 inhalation volume based on the inference of PM exposure in Beijing (3025 km, 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 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 inhalation volume over the whole region.
空气质量（AQ）与人们的健康直接相关，而实施有效的方法来获取污染细节并评估健康影响对于公共卫生管理非常重要。在本文中，我们设计了一个端到端的时空建模框架来估计逐像素的PM吸入量，称为ST-Exposure，该框架在以下方面展示了其实用性和优势：（1）结合使用固定和移动空气质量传感器，我们基于对北京（2018年6月19日至7月16日，面积3025平方公里）的PM暴露推断，以1公里×1公里和1小时的时空分辨率估计PM吸入量，平均绝对百分比误差（SMAPE）小于15%。（2）即使在时空覆盖稀疏的情况下，也能在城市尺度上以高分辨率（1公里×1公里，每小时）推断逐像素的PM吸入量。（3）提出了一种新的人口分布计算机制，该机制优于传统的基于人口普查的方法，并且能够更可靠地估计整个区域的总PM吸入量。
