|本期目录/Table of Contents|

[1]陈玉敏,魏 阳,常政威,等.基于遥感数据和XGBoost算法的31个城市NO2、CO2浓度比率变化特征[J].地球科学与环境学报,2023,45(06):1355-1367.[doi:10.19814/j.jese.2022.12024]
　CHEN Yu-min,WEI Yang,CHANG Zheng-wei,et al.Variation Characteristics of Concentration Ratio of Nitrogen Dioxide and Carbon Dioxide in 31 Cities, China Based on Remote Sensing Data and XGBoost Algorithm[J].Journal of Earth Sciences and Environment,2023,45(06):1355-1367.[doi:10.19814/j.jese.2022.12024]
点击复制

基于遥感数据和XGBoost算法的31个城市NO2、CO2浓度比率变化特征(PDF)
分享到：

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

卷:
第45卷
期数:
2023年第06期
页码:
1355-1367
栏目:
环境与可持续发展
出版日期:
2023-11-15

文章信息/Info

Title:
Variation Characteristics of Concentration Ratio of Nitrogen Dioxide and Carbon Dioxide in 31 Cities, China Based on Remote Sensing Data and XGBoost Algorithm
文章编号:
1672-6561(2023)06-1355-13
作者:
陈玉敏1; 魏 阳1; 常政威1; 张凌浩1; 刘洪利1; 刘雪原1; 曾 文2; 赵子翔3; 李春圆3; 米 潭3; 4; 詹 宇3; 4*
(1. 国网四川省电力公司电力科学研究院,四川 成都 610095; 2. 四川大学 灾后重建与管理学院,四川 成都 610207; 3. 四川大学 建筑与环境学院,四川 成都 610065; 4. 四川大学 碳中和未来技术学院,四川 成都 610065)
Author(s):
CHEN Yu-min1;  WEI Yang1;  CHANG Zheng-wei1;  ZHANG Ling-hao1;  LIU Hong-li1;  LIU Xue-yuan1;  ZENG Wen2;  ZHAO Zi-xiang3;  LI Chun-yuan3;  MI Tan3; 4;  ZHAN Yu3; 4*
(1. State Grid Sichuan Electric Power Company Electric Power Research Institute, Chengdu 610095, Sichuan, China; 2. Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu 610207, Sichuan, China; 3. College of Architecture and Environment, Sichuan University, Chengdu 610065, Sichuan, China; 4. College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, Sichuan, China)
关键词:
二氧化氮;  二氧化碳;  浓度比率;  遥感;  机器学习;  时空分布;  减污降碳;  城市
Keywords:
nitrogen dioxide;  carbon dioxide;  concentration ratio;  remote sensing;  machine learning;  spatiotemporal distribution;  reduction of pollution and carbon emission;  city
分类号:
X24
DOI:
10.19814/j.jese.2022.12024
文献标志码:
A
摘要:
研究城市二氧化氮(NO2)与二氧化碳(CO2)的共同排放水平对于实现城市减污降碳协同增效具有重要意义。NO2与CO2的人为排放具有同源性,二者的浓度比率可以体现城市的大气污染物和碳排放综合水平。基于XGBoost算法,利用对流层星载监测仪(TROPOMI)、轨道碳观测2号(OCO-2)和3号(OCO-3)卫星的遥感数据以及相关环境协变量,重构全国2019～2020年NO2垂直柱浓度(XNO2)和CO2垂直柱浓度(XCO2)的高分辨率数据集,并分析31个城市的XNO2年均值与ΔXCO2(XCO2观测值与背景值之差)的比率(XNO2/ΔXCO2)变化情况。结果表明:XNO2及XCO2重构模型的验证决定系数(R2)分别为0.90和0.96; 城市XNO2和ΔXCO2值成正相关关系(相关系数为0.66); 发展水平较高的城市相对发展水平一般的城市具有更高的XNO2/ΔXCO2值; 受城市能源利用水平的差异影响,发展水平较高的城市XNO2/ΔXCO2值与城市GDP、常住人口数量及空气污染水平成负相关关系(相关系数分别为-0.35、-0.38、-0.10),而发展水平一般的城市则成正相关关系(相关系数分别为 0.37、0.32、0.47)。XNO2/ΔXCO2值与城市污染治理和能源利用水平相关,研究结果可为城市控制空气污染提供数据支撑。
Abstract:
Studying the joint emission levels of nitrogen dioxide(NO2)and carbon dioxide(CO2)in cities is of great significance for achieving synergistic pollution and carbon reduction, and improving overall efficiency. The anthropogenic emissions of NO2 and CO2 are closely related, and their concentration ratios can reflect the comprehensive level of atmospheric pollutants and carbon emissions in cities. The high-resolution datasets of NO2 vertical column concentration(XNO2)and CO2 vertical column concentration(XCO2)from 2019 to 2020 in China were reconstructed by using TROPOMI, OCO-2, and OCO-3 satellite remote sensing data and environmental auxiliary variables based on XGBoost algorithm, and the concentration ratios of XNO2 annual average and ΔXCO2(the difference between XCO2 observation and background values)in 31 cities(abbreviated XNO2/ΔXCO2)were analyzed. The results show that the algorithm is superior performance in reconstructing satellite XNO2 and XCO2 with R2 of 0.90 and 0.96 for sample-based holdout-validation, respectively. Urban XNO2 is positively correlated with ΔXCO2(r=0.66). Cities in advanced development level have higher XNO2/ΔXCO2 compared with cities in averaged development level. Due to the difference in energy utilization efficiency, the XNO2/ΔXCO2 of cities in advanced development level is negatively correlated with urban GDP, permanent population and air pollution level(r=-0.35, -0.38, and -0.10), while that of cities in averaged development level is positively correlated(r=0.37, 0.32, and 0.47). The XNO2/ΔXCO2 is related to the level of urban pollution control and energy utilization, and the results can provide support for urban air pollution control.

参考文献/References:

[1] 何 月,绳梦雅,雷莉萍,等.长三角地区大气NO2和CO2浓度的时空变化及驱动因子分析[J].中国环境科学,2022,42(8):3544-3553.
HE Yue,SHENG Meng-ya,LEI Li-ping,et al.Driving Factors and Spatio-temporal Distribution on NO2 and CO2 in the Yangtze River Delta[J].China Environmental Science,2022,42(8):3544-3553.
[2] 万芸菲,崔阳阳,吴雪芳,等.京津冀区域二氧化碳排放特征及其与大气污染物协同减排潜力分析[J].首都师范大学学报(自然科学版),2022,43(4):46-52.
WAN Yun-fei,CUI Yang-yang,WU Xue-fang,et al.Characteristics of Carbon Dioxide Emission in Beijing-Tianjin-Hebei Region and Its Synergistic Reduction Potential with Air Pollutants[J].Journal of Capital Normal University(Natural Science Edition),2022,43(4):46-52.
[3] GOLDBERG D L,LU Z,ODA T,et al.Exploiting OMI NO2 Satellite Observations to Infer Fossil-fuel CO2 Emissions from U.S.Megacities[J].Science of the Total Environment,2019,695:133805.
[4] LIU F,DUNCAN B N,KROTKOV N A,et al.A Methodology to Constrain Carbon Dioxide Emissions from Coal-fired Power Plants Using Satellite Observations of Co-emitted Nitrogen Dioxide[J].Atmo-spheric Chemistry and Physics,2020,20(1):99-116.
[5] MI T,TANG D,FU J B,et al.Data Augmentation for Bias Correction in Mapping PM2.5 Based on Satellite Retrievals and Ground Observations[J].Geoscience Frontiers,2024,15(1):101686.
[6] DONG J D,CAI X B,TIAN L Q,et al.Satellite-based Estimates of Daily NO2 Exposure in Urban Agglome-rations of China and Application to Spatio-temporal Characteristics of Hotspots[J].Atmospheric Environment,2023,293:119453.
[7] NOËL S,REUTER M,BUCHWITZ M,et al.Retrie-val of Greenhouse Gases from GOSAT and GOSAT-2 Using the FOCAL Algorithm[J].Atmospheric Mea-surement Techniques,2022,15(11):3401-3437.
[8] DOUGHTY R,KUROSU T P,PARAZOO N,et al.Global GOSAT,OCO-2,and OCO-3 Solar-induced Chlorophyll Fluorescence Datasets[J].Earth System Science Data,2022,14(4):1513-1529.
[9] YANG D X,HAKKARAINEN J,LIU Y,et al.Detection of Anthropogenic CO2 Emission Signatures with TanSat CO2 and with Copernicus Sentinel-5 Precursor(S5P)NO2 Measurements:First Results[J].Advances in Atmospheric Sciences,2023,40(1):1-5.
[10] 程韵初,吴 莹.基于OMI资料的中国对流层NO2柱浓度时空变化及其影响因子分析[J].地球物理学进展,2020,35(5):1644-1650.
CHENG Yun-chu,WU Ying.Spatiotemporal Changes of Tropospheric NO2 Vertical Column Densities in China Based on OMI Data and Its Influencing Factors[J].Progress in Geophysics,2020,35(5):1644-1650.
[11] 王景芝,袁金国,郭 豪,等.基于TROPOMI的京津冀NO2柱浓度时空变化分析[J].环境科学与技术,2022,45(3):21-31.
WANG Jing-zhi,YUAN Jin-guo,GUO Hao,et al.Spatio-temporal Analysis of NO2 Column Density in Beijing-Tianjin-Hebei Region Based on TROPOMI[J].Environmental Science & Technology,2022,45(3):21-31.
[12] WU Y Y,DI B F,LUO Y Z,et al.A Robust Approach to Deriving Long-term Daily Surface NO2 Le-vels Across China:Correction to Substantial Estimation Bias in Back-extrapolation[J].Environment International,2021,154:106576.
[13] FU J B,TANG D,GRIENEISEN M L,et al.A Machine Learning-based Approach for Fusing Measurements from Standard Sites,Low-cost Sensors,and Sa-tellite Retrievals:Application to NO2 Pollution Hot-spot Identification[J].Atmospheric Environment,2023,302:119756.
[14] 吴长江,雷莉萍,曾招城.不同卫星反演的大气CO2浓度差异时空特征分析[J].中国科学院大学学报,2019,36(3):331-337.
WU Chang-jiang,LEI Li-ping,ZENG Zhao-cheng.Spatiotemporal Analysis of Differences Among Atmospheric CO2 Concentrations Retrieved from Diffe-rent Satellite Observations[J].Journal of University of Chinese Academy of Sciences,2019,36(3):331-337.
[15] YANG E G,KORT E A,OTT L E,et al.Using Spa-ce-based CO2 and NO2 Observations to Estimate Urban CO2 Emissions[J].Journal of Geophysical Research:Atmospheres,2023,128(6):e2022JD037736.
[16] REUTER M,BUCHWITZ M,SCHNEISING O,et al.Towards Monitoring Localized CO2 Emissions from Space:Co-located Regional CO2 and NO2 Enhancements Observed by the OCO-2 and S5P Satellites[J].Atmospheric Chemistry and Physics,2019,19(14):9371-9383.
[17] 莫 露,巫兆聪,张 熠.中国XCO2时空分布与影响因素分析[J].中国环境科学,2021,41(6):2562-2570.
MO Lu,WU Zhao-cong,ZHANG Yi.Spatial and Temporal Variations of XCO2 in China and Its Influencing Factors Analysis[J].China Environmental Scien-ce,2021,41(6):2562-2570.
[18] PARK H,JEONG S,PARK H,et al.An Assessment of Emission Characteristics of Northern Hemisphere Cities Using Spaceborne Observations of CO2,CO,and NO2[J].Remote Sensing of Environment,2021,254:112246.
[19] SILVA S J,ARELLANO A F.Characterizing Regio-nal-scale Combustion Using Satellite Retrievals of CO,NO2 and CO2[J].Remote Sensing,2017,9(7):744.
[20] FU Y,SUN W B,FAN D Q,et al.An Assessment of China's Industrial Emission Characteristics Using Sa-tellite Observations of XCO2,SO2,and NO2[J].Atmospheric Pollution Research,2022,13(8):101486.
[21] WANG C J,WANG T,WANG P C,et al.Comparison and Validation of TROPOMI and OMI NO2 Observations over China[J].Atmosphere,2020,11(6):636.
[22] IALONGO I,VIRTA H,ESKES H,et al.Comparison of TROPOMI/Sentinel-5 Precursor NO2 Observations with Ground-based Measurements in Helsinki[J].Atmospheric Measurement Techniques,2020,13(1):205-218.
[23] ESA.Sentinels Scientific Data Hub[EB/OL].(2021-08-12)[2022-12-12].https:∥scihub.copernicus.eu/.
[24] 王震山,绳梦雅,肖 薇,等.基于多源碳卫星融合产品的中国地区XCO2与人为CO2排放时空变化[J].中国环境科学,2023,43(3):1053-1063.
WANG Zhen-shan,SHENG Meng-ya,XIAO Wei,et al.Spatiotemporal Changes of XCO2 and Anthropogenic CO2 Emission in China Based on Multi-source Carbon Satellite Fusion Product[J].China Environmental Science,2023,43(3):1053-1063.
[25] HAKKARAINEN J,IALONGO I,ODA T,et al.Building a Bridge:Characterizing Major Anthropogenic Point Sources in the South African Highveld Region Using OCO-3 Carbon Dioxide Snapshot Area Maps and Sentinel-5P/TROPOMI Nitrogen Dioxide Colu-mns[J].Environmental Research Letters,2023,18(3):035003.
[26] ELDERING A,TAYLOR T E,O'DELL C W,et al.The OCO-3 Mission:Measurement Objectives and Expected Performance Based on 1 Year of Simulated Data[J].Atmospheric Measurement Techniques,2019,12(4):2341-2370.
[27] ZHOU M Q,NI Q C,CAI Z N,et al.CO2 in Beijing and Xianghe Observed by Ground-based FTIR Colu-mn Measurements and Validation to OCO-2/3 Satellite Observations[J].Remote Sensing,2022,14(15):3769.
[28] NASA.NASA Gesdisc Data Archive[EB/OL].(2021-10-07)[2022-12-12].https:∥oCO2.gesdisc.eosdis.nasa.gov/data/OCO2_DATA/OCO2_L2_Lite_FP.10r/.
[29] HE C P,JI M R,LI T,et al.Deriving Full-coverage and Fine-scale XCO2 Across China Based on OCO-2 Satellite Retrievals and Carbon Tracker Output[J].Geophysical Research Letters,2022,49(12):e2022-GL098435.
[30] WEI J,LIU S,LI Z Q,et al.Ground-level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence[J].Environmental Science & Technology,2022,56(14):9988-9998.
[31] KIRKLAND E J.Bilinear Interpolation[M]∥KIRKLAND E J.Advanced Computing in Electron Microscopy.Boston:Springer,2010:261-263.
[32] 项晓铭,崔珍珍,张 浩,等.中国重点城市NO2排放规律与驱动分析[J].环境科学与技术,2022,45(7):30-45.
XIANG Xiao-ming,CUI Zhen-zhen,ZHANG Hao,et al.Analysis of Laws of NO2 Emission and Driving Factors of China's Key Cities[J].Environmental Science & Technology,2022,45(7):30-45.
[33] SRTM.The Shuttle Radar Topography Mission[EB/OL].(2021-10-25)[2022-12-12].http:∥dwtkns.com/srtm30m/.
[34] DIDAN K.MOD13Q1 MODIS/Terra Vegetation Indices 16-day L3 Global 250 m SIN Grid V006[EB/OL].(2021-10-16)[2022-12-12].https:∥lpdaac.usgs.gov/products/mod13q1v006/.
[35] CIESIN.Gridded Population of the World,Version 4(GPWv4):Population Density,Revision 11[EB/OL].(2021-07-25)[2022-12-12].https:∥sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev-11.
[36] ESA.Land Cover CCI Product User Guide Version 2.0[EB/OL].(2017-04-10)[2022-12-12].https:∥climate.esa.int/media/documents/CCI_Land_Cover_PUG_v2.0.pdf.
[37] ECMWF.ECMWF Reanalysis v5(ERA5)[EB/OL].(2021-06-20)[2022-12-12].https:∥www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5.
[38] LIU Y,LI C Y,LIU D R,et al.Deriving Hourly Full-coverage PM2.5 Concentrations Across China's Sichuan Basin by Fusing Multisource Satellite Retrie-vals:A Machine-learning Approach[J].Atmospheric Environment,2022,271:118930.
[39] ZHAO Z,LU Y,ZHAN Y,et al.Long-term Spatiotemporal Variations in Surface NO2 for Beijing Reconstructed from Surface Data and Satellite Retrievals[J].Science of the Total Environment,2023,904:166693.
[40] 陈玉敏,唐 伟,刘雪原,等.基于卫星遥感的四川省XCO2高分辨率时空分布[J].环境科学与技术,2023,46(2):133-140.
CHEN Yu-min,TANG Wei,LIU Xue-yuan,et al.High-resolution XCO2 Spatiotemporal Distribution in Sichuan Province Based on Multi-source Statellite Remote Sensing[J].Environmental Science & Technology,2023,46(2):133-140.
[41] 李少亭,王雪瑞.XGBoost模型在新冠疫情预测中的研究应用[J].小型微型计算机系统,2021,42(12):2465-2472.
LI Shao-ting,WANG Xue-rui.Research and Application of XGBoost in Prediction of Novel Coronavirus Epidemic[J].Journal of Chinese Computer Systems,2021,42(12):2465-2472.
[42] CHEN T Q,GUESTRIN C.XGBoost:A Scalable Tree Boosting System[C]∥KRISHNAPURAM B,SHAH M,SMOLA A,et al.KDD'16:Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco:Association for Computing Machinery,2016:785-794.
[43] PEI Z,HAN G,MA X,et al.A Method for Estimating the Background Column Concentration of CO2 Using the Lagrangian Approach[J].IEEE Transactions on Geoscience and Remote Sensing,2022,60:4108112.
[44] AN N,MUSTAFA F,BU L B,et al.Monitoring of Atmospheric Carbon Dioxide Over Pakistan Using Sa-tellite Dataset[J].Remote Sensing,2022,14(22):5882.
[45] ZHANG Q,BOERSMA K F,ZHAO B,et al.Quanti-fying Daily NOx and CO2 Emissions from Wuhan Us-ing Satellite Observations from TROPOMI and OCO-2[J].Atmospheric Chemistry and Physics,2023,23(1):551-563.
[46] JIANG K,YU H,CHEN L,et al.An Advanced,Ammonia-based Combined NOx/SOx/CO2 Emission Control Process Towards a Low-cost,Clean Coal Technology[J].Applied Energy,2020,260:114316.

相似文献/References:

备注/Memo

备注/Memo:
收稿日期:2022-12-11; 修回日期:2023-07-04
基金项目:国家重点研发计划项目(2017YFC1502903-1B); 国家自然科学基金项目(22076129)
作者简介:陈玉敏(1997-),女,四川眉山人,初级工程师,E-mail:chenyumin@163.com。
*通讯作者:詹 宇(1986-),男,浙江台州人,副教授,博士研究生导师,理学博士,博士后,E-mail:yzhan@scu.edu.cn。
更新日期/Last Update: 2023-12-01