中国区域2001―2020年近地面臭氧浓度估算  

Estimation of near-surface ozone concentrations in China from 2001 to 2020

作　　者：黄凯 骆文慧 万城 宫明艳 麻金继 HUANG Kai;LUO Wenhui;WAN Cheng;GONG Mingyan;MA Jinji(School of Geography and Tourism,Anhui Normal University,Wuhu 241002,China;Engineering Technology Research Center of Resources Environment and GIS,Anhui Province,Wuhu 241002,China;School of Physics and Electronic Information,Anhui Normal University,Wuhu 241002,China)

机构地区：[1]安徽师范大学地理与旅游学院,安徽芜湖241002 [2]资源环境与地理信息工程安徽省工程技术研究中心,安徽芜湖241002 [3]安徽师范大学物理与电子信息学院,安徽芜湖241002

出　　处：《大气与环境光学学报》2024年第6期646-664,共19页Journal of Atmospheric and Environmental Optics

基　　金：国家自然科学基金项目(42271372)。

摘　　要：随着大气环境治理的不断推进,颗粒物污染显著下降,但臭氧污染问题却日益严峻,因此构建中国区域的长时间地面臭氧数据集对了解地面臭氧的分布传输、推动细颗粒物与臭氧协同治理具有积极影响。本研究结合极端随机树和极端梯度提升两种机器学习算法的优势,使用臭氧监测数据、遥感产品以及大气再分析数据构建了中国地表每日最大8 h平均臭氧(MDA8O_(3))浓度估算模型,从样本、空间、时间进行模型精度验证,并分年度、季度、历史尺度、区域尺度验证了模型的时空适用性,并衍生了中国区域全覆盖的2001―2020年臭氧数据产品。结果表明:(1)结合两种算法优势的臭氧估算模型表现出优良的精度,三种精度验证的决定系数R^(2)都在0.89~0.95之间,均方根误差(RMSE)为10.73~15.56μg/m^(3);(2)多种时空验证的结果表明本研究构建的模型能够应用于中国区域大范围、长时间的臭氧估算工作中;(3)本研究构建的臭氧数据产品能够较好地反映地面臭氧的月级、年级的时空分异,更直观地显示臭氧浓度的时空变化。With the continuous advancement of atmospheric environmental governance,particulate matter pollution has significantly decreased,but at the same time,ozone pollution has become increasingly severe.Therefore,constructing a long-term ground-level ozone dataset for China is essential for understanding the distribution and transmission of ground-level ozone and promoting the coordinated control of fine particulate matter and ozone.In this study,by combining the advantages of two machine learning algorithms,extreme random tree and extreme gradient boosting,we use ozone monitoring data,remote sensing products,and atmospheric reanalysis data to construct a daily maximum eight-hour average ozone(MDA8 O_(3))concentration estimation model for China's surface.The model accuracy is validated from sample,space,and time perspectives,and its spatiotemporal applicability is verified at annual,quarterly,historical,and regional scales.And at last,a China-wide ozone data product covering the period of 2001 to 2020 is derived.The results show that:(1)the ozone estimation model,which combines the advantages of the two algorithms,exhibits excellent accuracy,with R^(2) ranging from 0.89 to 0.95 and root mean square error(RMSE)ranging from 10.73 to 15.56μg/m^(3);(2)The multiple spatiotemporal verifications indicate that the model constructed in this study can be applied to large-scale,long-term ozone estimation work in the China region;(3)The ozone data product constructed in this study can well reflect the spatiotemporal differentiation of ground-level ozone at the monthly and annual scales,and display the spatiotemporal changes of ozone concentration intuitively.

关 键 词：大气遥感 极端随机树 极端梯度提升 臭氧 时空关联 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] X515[自动化与计算机技术—控制科学与工程]