Faculty of Civil Engineering and Transportation, University of Isfahan, Azadi Square, Isfahan, 8174673441, Iran.
Environ Monit Assess. 2023 Feb 9;195(3):377. doi: 10.1007/s10661-023-10951-1.
High-resolution mapping of PM2.5 concentration over Tehran city is challenging because of the complicated behavior of numerous sources of pollution and the insufficient number of ground air quality monitoring stations. Alternatively, high-resolution satellite Aerosol Optical Depth (AOD) data can be employed for high-resolution mapping of PM2.5. For this purpose, different data-driven methods have been used in the literature. Recently, deep learning methods have demonstrated their ability to estimate PM2.5 from AOD data. However, these methods have several weaknesses in solving the problem of estimating PM2.5 from satellite AOD data. In this paper, the potential of the deep ensemble forest method for estimating the PM2.5 concentration from AOD data was evaluated. The results showed that the deep ensemble forest method with [Formula: see text] gives a higher accuracy of PM2.5 estimation than deep learning methods ([Formula: see text]) as well as classic data-driven methods such as random forest ([Formula: see text]). Additionally, the estimated values of PM2.5 using the deep ensemble forest algorithm were used along with ground data to generate a high-resolution map of PM2.5. Evaluation of produced PM2.5 map revealed the good performance of the deep ensemble forest for modeling the variation of PM2.5 in the city of Tehran.
在德黑兰市进行 PM2.5 浓度的高分辨率测绘具有挑战性，因为存在大量污染源，且地面空气质量监测站的数量不足，这导致其行为非常复杂。或者，可以使用高分辨率卫星气溶胶光学深度（AOD）数据进行 PM2.5 的高分辨率测绘。为此，文献中已经使用了不同的数据驱动方法。最近，深度学习方法已经证明了它们从 AOD 数据估算 PM2.5 的能力。然而，这些方法在解决从卫星 AOD 数据估算 PM2.5 的问题方面存在一些弱点。在本文中，评估了深度集成森林方法从 AOD 数据估算 PM2.5 浓度的潜力。结果表明，与深度学习方法（[Formula: see text]）以及经典数据驱动方法（如随机森林（[Formula: see text]））相比，具有[Formula: see text]的深度集成森林方法可以更准确地估算 PM2.5。此外，还使用深度集成森林算法估算的 PM2.5 值与地面数据一起生成 PM2.5 的高分辨率地图。对生成的 PM2.5 地图进行评估表明，深度集成森林在模拟德黑兰市 PM2.5 的变化方面表现良好。
