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改进的表面 NO₂ 检索:中国 (2018-2023) 双层机器学习模型构建和时空特征分析 (2018-2023)

Wei Wang¹, Bingqian Li¹, Biyan Chen¹

¹School of Geosciences and Info-Physics, Central South University, Changsha, China.

Journal of environmental management

|April 29, 2025

概括

这项研究开发了一种新的双层机器学习 (DLML) 模型,通过考虑其垂直结构来准确估计表面的二氧化 (NO2) 水平. DLML模型显著提高了中国各地的NO2监测准确度.

科学领域:

大气化学和物理大气化学和物理
环境监测环境监测
机器学习应用机器学习应用

背景情况:

表面二氧化 (SNO2) 是一种具有重大健康和环境影响的关键空气污染物.
现有的SNO2检索模型不充分考虑NO2的垂直结构,导致不准确.
传统的机器学习模型与SNO2和热层NO2柱 (XNO2) 复杂的时空动态作斗争.

研究的目的:

通过整合NO2垂直分层特征和时空变化机制,提高SNO2水平逆转的准确性.
开发和验证一种新的双层机器学习 (DLML) 框架,用于估计中国各地的SNO2水平.
分析2018年至2023年中国SNO2水平的时间和空间变化模式.

主要方法:

开发一个双层机器学习 (DLML) 框架,利用光梯度增强机 (LGBM) 和极端随机森林 (ERF).
将NO2垂直分层和时空变化机制纳入DLML模型.
在2018-2023年期间,中国各地的SNO2水平估计.

主要成果:

在空间时空交叉验证中,DLML模型表现出卓越的性能,在空间时空交叉验证中R2为0.87,比传统模型提高10%.
平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 分别降至大约4.24μg/m3和5.79μg/m3.
从中部/东部沿海地区到周边地区,SNO2水平呈现下降趋势,与世卫组织空气质量指南保持一致;观察到U形时间变化,冬季 (一月,十二月) 达到峰值,夏季 (六月至八月) 达到最低.

结论:

DLML框架有效地整合了NO2的垂直结构和时空动态,以准确地估计SNO2.
冬季气象条件显著影响SNO2的变化,区域经济和工业活动导致武汉和长江三角洲等地区的升水平.
该研究提供了一种可靠的SNO2监测方法,这对于了解和减轻空气污染影响至关重要.

关键词:

非常随机的森林非常随机的森林.轻GBMM 轻GBMM 的时间 NO ((2)) 的情况.遥感是一种远程传感.太极度多样性 (TROPOMI) 是一种

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