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首页> 外文期刊>Atmospheric chemistry and physics >Himawari-8-derived diurnal variations in ground-level PM 2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)
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Himawari-8-derived diurnal variations in ground-level PM 2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

机译：Himawari-8-衍生的地面PM 2.5污染在中国的污染，使用快节时空光梯度升压机（LightGBM）

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摘要

Fine particulate matter with a diameter of less than 2.5? μm ( PM 2.5 ) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM 2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM 2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM 2.5 . Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM 2.5 dataset for China (i.e., ChinaHigh PM 2.5 ) at a 5? km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM 2.5 estimates (number of data samples? = ?1?415?188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV- R 2 ? = ?0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49? μg?m ?3 , respectively. Our model captures well the PM 2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00?LT (GMT + 8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.
机译：细颗粒物质的直径小于2.5？ μm（2.5）已被用作重要的大气环境参数，主要是因为它对人类健康的影响。 PM 2.5受天然和人为因素的影响，通常具有强大的昼夜变异。这些信息有助于了解空气污染的原因，以及我们对其的适应。大多数现有的PM 2.5产品来自极性轨道卫星。本研究利用了下一代地球静止气象卫星喜马哇基-8 / Ahi（先进的Himawari Imager）来记录下午2.5的日变化。鉴于巨大的卫星数据，基于梯度升压的思想，一种高效的基于树的轻梯度升压机（LightGBM）方法，通过涉及空气污染的时空特性，即空时光GBM（STLG）模型，开发。每小时PM 2.5中国的数据集（即Chinahigh PM 2.5）在5？ KM空间分辨率是基于Himawari-8 / Ahi气溶胶产品的衍生带有额外的环境变量。每小时PM 2.5估计（数据样本的数量？=？1？415？188）与中国的地面测量有很好的相关性（交叉验证系数，CV-R 2？= 0.85），具有根性意义 - Square Error（RMSE）和232和8.49的平均误差（MAE）？ μg？m？3分别。我们的型号捕获PM 2.5昼夜变化，显示污染在早晨逐渐增加，达到约10：00的峰值（GMT + 8），然后稳步减少直到日落。所提出的方法优于大多数传统的统计回归和基于树的机器学习模型，在速度和记忆方面具有更低的计算负担，使其最适合常规污染监测。

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