School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China.
School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China; Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, Hubei, 430079, China.
Environ Pollut. 2022 Aug 1;306:119347. doi: 10.1016/j.envpol.2022.119347. Epub 2022 Apr 26.
Intra-urban pollution monitoring requires fine particulate (PM) concentration mapping at ultrahigh-resolution (dozens to hundreds of meters). However, current PM concentration estimation, which is mainly based on aerosol optical depth (AOD) and meteorological data, usually had a low spatial resolution (kilometers) and severe spatial missing problem, cannot be applied to intra-urban pollution monitoring. To solve these problems, top-of-atmosphere reflectance (TOAR), which contains both the information about land and atmosphere and has high resolution and large spatial coverage, may be efficiently used for PM estimation. This study aims to systematically evaluate the feasibility of retrieving ultrahigh-resolution PM concentration at a large scale (national level) from TOAR. Firstly, we make a detailed discussion about several important but unsolved theoretic problems on TOAR-based PM retrieval, including the band selection, scale effect, cloud impact, and mapping quality evaluation. Secondly, four types and eight retrieval models are compared in terms of quantitative accuracy, mapping quality, model generalization, and model efficiency, with the pros and cons of each type summarized. Deep neural network (DNN) model shows the highest retrieval accuracy, and linear models were the best in efficiency and generalization. As a compromise, ensemble learning shows the best overall performance. Thirdly, using the highly accurate DNN model (cross-validated R equals 0.93) and through combining Landsat 8 and Sentinel 2 images, a 90 m and ∼4-day resolution PM product was generated. The retrieved maps were used for analyzing the fine-scale interannual pollution change inner the city and the pollution variations during novel coronavirus disease 2019 (COVID-19). Results of this study proves that ultrahigh resolution can bring new findings of intra-urban pollution change, which cannot be observed at previous coarse resolution. Lastly, some suggestions for future ultrahigh-resolution PM mapping research were given.
城市内的污染监测需要对超细颗粒物 (PM) 进行超高分辨率 (几十到几百米) 的浓度测绘。然而，目前主要基于气溶胶光学深度 (AOD) 和气象数据的 PM 浓度估计通常空间分辨率较低 (公里级)，并且存在严重的空间缺失问题，无法应用于城市内的污染监测。为了解决这些问题，可能会有效地利用大气顶层反射率 (TOAR) 进行 PM 估算。TOAR 包含陆地和大气的信息，具有高分辨率和大空间覆盖范围。本研究旨在系统地评估从 TOAR 反演超高分辨率 PM 浓度的可行性。首先，我们详细讨论了基于 TOAR 的 PM 反演中几个重要但尚未解决的理论问题，包括波段选择、尺度效应、云影响和制图质量评估。其次，从定量精度、制图质量、模型泛化和模型效率等方面比较了四类八种反演模型，总结了每种模型的优缺点。深度神经网络 (DNN) 模型的反演精度最高，线性模型在效率和泛化能力方面表现最好。作为一种折衷，集成学习表现出最佳的整体性能。第三，利用高度准确的 DNN 模型（交叉验证 R 等于 0.93）并结合 Landsat 8 和 Sentinel 2 图像，生成了 90 m 和 ∼4 天分辨率的 PM 产品。所得到的反演地图用于分析城市内部的细尺度年际污染变化以及新型冠状病毒病 2019 (COVID-19) 期间的污染变化。本研究的结果证明，超高分辨率可以带来城市内污染变化的新发现，这些变化在以前的粗分辨率下是无法观察到的。最后，对未来超高分辨率 PM 制图研究提出了一些建议。
