摘要
由于二氧化氮(NO_(2))在大气中的存活寿命较短,卫星遥感反演的对流层NO_(2)柱浓度与近地面NO_(2)浓度关系密切。欧洲航天局(ESA)S5P卫星的对流层检测仪(TROPOMI)载荷提供了目前最高空间分辨率的对流层NO_(2)数据,其在近地面NO_(2)浓度估算方面的潜在优势亟待检验。为此,本文采用极限梯度提升(XGBoost)算法和4年(2018—2021年)的TROPOMI/臭氧检测仪(OMI)数据估算了我国近地面NO_(2)浓度并开展了对比性分析。结果表明:1)TROPOMI的估算结果在精度和空间覆盖度两个方面,均明显高于OMI的结果;2)OMI数据由于自身空间分辨率的限制,无法和TROPOMI一样识别出NO_(2)浓度高值区附近的空间分布细节,导致其估算结果存在更严重的高估或低估。进一步,针对机器学习方法估算近地面NO_(2)普遍存在高值低估的现象,通过集成模型进行优化,得到了更优的结果(R^(2)=0.85,slope为0.89)。该研究结果有利于促进卫星遥感在近地面NO_(2)浓度估算与暴露评估领域的深入应用。
Objective Nitrogen dioxide(NO_(2))in the atmosphere has an important impact on air quality and climate change,and ground-level NO_(2) will directly affect human health.China is one of the regions with high concentrations of NO_(2) in the world.Long-term surface NO_(2) concentration data has been provided by China Environmental Monitoring Station since 2013.In addition,the satellite data can make up for the lack of coverage of ground stations.Compared with the previous ozone detector(OMI)sensor,tropospheric detector(TROPOMI)has higher data coverage and spatial resolution,but its potential for ground-level NO_(2) estimation needs to be proved,and the underestimation of the estimation model predicting high-value samples needs to be optimized.The purpose of this paper is to use machine learning algorithms to estimate ground-level NO_(2) concentration in China based on satellite observation data and obtain 0.05-degree NO_(2) concentration raster data from 2014 to 2021.On this basis,a systematic comparative study is carried out on the difference in the estimation results of TROPOMI and OMI sensor observations,and an optimization model is established to optimize the underestimation of the conventional machine learning model in the high-value area.Methods The dataset in this paper contains the observations of ground-level NO_(2) concentration from ground stations,the tropospheric NO_(2) column concentration provided by OMI and TROPOMI which come from European Space Agency and Google Earth Engine,and auxiliary data that contains meteorological data of ERA5,population data,surface elevation data,and land use data.Data preprocessing includes assigning station data to the nearest grid and resampling data with different spatial resolutions to 0.05 degrees.The dataset and the algorithm are used to build a model with the algorithm named XGBoost,which is optimized on the basis of GBDT,so as to have higher prediction accuracy.The features of the model are selected by variance inflation factor(VIF)and analyzed by shapley additive
作者
周文远
秦凯
何秦
王璐瑶
罗锦洪
谢卧龙
Zhou Wenyuan;Qin Kai;He Qin;Wang Luyao;Luo Jinhong;Xie Wolong(School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,Jiangsu,China;Xi'an Institute for Innovative Earth Environment Research,Xi'an 710061,Shaanxi,China;Shanxi Academy of Eco-Environmental Planning and Technology,Taiyuan 030000,Shanxi,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2024年第6期123-134,共12页
Acta Optica Sinica
基金
国家自然科学基金(42375125)。
关键词
遥感与传感器
近地面二氧化氮浓度估算
极限梯度提升算法
特征分析
估算优化
remote sensing and sensors
estimation of ground-level NO_(2)concentration
extreme gradient boosting algorithm
feature analysis
optimization of estimation
