Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Department of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Environ Pollut. 2022 Aug 1;306:119425. doi: 10.1016/j.envpol.2022.119425. Epub 2022 May 7.
Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM) and <2.5 μm (PM) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM and PM were R = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM and PM were R = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.
在过去几十年中，东亚经济的快速增长、工业化和城市化导致了频繁的空气污染事件。最近，地球静止卫星传感器的气溶胶数据已被用于每小时监测地面颗粒物（PM）浓度。然而，许多研究都集中在使用历史数据集来开发 PM 估算模型，这往往会降低模型对新日期未见数据的预测能力。为了解决这个问题，本研究提出了一种新的实时学习（RTL）方法，使用地球静止轨道海洋成像仪（GOCI）的每小时气溶胶数据和数值模型输出，估算东北亚白天 <10μm（PM）和 <2.5μm（PM）的气溶胶浓度。使用 10 折交叉验证（CV）评估了三种具有不同加权策略的方案。考虑浓度和时间作为加权因素的 RTL 模型（即方案 3）在小时和月尺度上的 10 折 CV 性能均有一致的提高。PM 和 PM 的实时校准结果分别为 R=0.97 和 0.96，相对均方根误差（rRMSE）分别为 12.1%和 12.0%，PM 和 PM 的 10 折 CV 结果分别为 R=0.73 和 0.69，rRMSE 分别为 41.8%和 39.6%。这些结果优于以往基于小时尺度历史数据的离线模型的结果。此外，我们在不使用陆基变量的情况下估算了海洋中的 PM 浓度，并清楚地展示了 PM 的传输随时间的变化。由于所提出的模型是基于 RTL 方法，因此现场监测点的密度可能是一个主要的不确定性因素。本研究表明，在低密度区域误差较高，而在高密度区域误差较低。可以采用所提出的方法进行实时监测地面 PM 浓度，并进行不确定性分析，以确保获得最佳结果。
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