基于甲醛卫星遥感的城市人为源挥发性有机物排放研究

甲醛（HCHO）是大气中的挥发性有机化合物（Volatile Organic Compounds，VOCs）氧化过程的中间产物，卫星观测的HCHO柱浓度通常与非甲烷挥发性有机化合物（Non-Methane Volatile Organic Compounds，NMVOCs）的排放密切相关。大气中甲烷的氧化是HCHO柱浓度的背景源，而NMVOCs（如异戊二烯）的快速氧化会导致HCHO柱浓度的明显升高。NMVOCs是城市大气中臭氧（O₃）和细颗粒物（PM_2.5）的重要前体物。NMVOCs种类繁多，且对所有物种进行大范围的地面监测十分困难，而HCHO柱浓度是监测NMVOCs排放的重要示踪物。但是，关联HCHO柱浓度和人为源NMVOCs排放的研究较少。

近年来，随着城市化的快速发展，城市地区的NMVOCs等大气污染物的人为源排放显著增加。准确监测和评估城市区域的NMVOCs等大气污染物的人为源排放，对于理解城市空气质量的变化、制定有效的污染控制策略至关重要。遥感技术的进步极大地提升了我们对城市区域及其大气环境的理解。特别是现在，卫星传感器的激增、高性能的计算平台和人工智能算法的发展，为大范围、高精度的城市NMVOCs等大气污染监测及分析提供了有力的支撑。基于卫星HCHO柱浓度等多源卫星数据，可以更好的理解城市区域中，NMVOCs等大气污染物的人为源排放特征。

本研究综合利用了多源卫星遥感数据、大气化学模式、过采样算法和机器学习等，进行HCHO和NO₂大气污染的高空间分辨率监测，然后从城市（以北京市为例）、国家（以中国为例）和全球等多个尺度分析了城市化、HCHO柱浓度、NMVOCs等大气污染物人为源排放的特征和联系。主要的研究成果如下：

北京市的HCHO和NO₂大气污染特征分析。实验选取了多种卫星遥感数据，包括北京2019年附近的可见光红外成像辐射计套件（Visible Infrared Imaging Radiometer Suite，VIIRS）夜间灯光数据、对流层监测仪（Tropospheric Monitoring Instrument，TROPOMI）的HCHO和NO₂柱浓度时间序列数据、Landsat 8 陆地成像仪（Operational Land Imager，OLI）图像数据和珞珈1号科学实验卫星（Luojia 1-01，LJ1-01）夜间灯光数据。基于过采样算法得到了北京夏季TROPOMI HCHO和NO₂柱浓度分布，其不确定性较低，均值分别为0.05和0.03。使用时间序列多源卫星数据和随机森林算法，分类并提取了北京市的不透水面信息，总体精度高达96%。通过线性相关性分析和特征重要性评估了6个表示城市化的夜间灯光指标，发现VIIRS夜间灯光辐射与HCHO、NO₂污染有高的正相关关系，相关系数分别为0.96和0.85；VIIRS夜间灯光辐射也表现出较高的特征重要性。VIIRS夜间灯光辐射可以反映城市区域的HCHO等大气污染物的排放特征。

在北京实验的基础上，进行了中国范围内的人为源NMVOCs排放和季节TROPOMI HCHO柱浓度特征研究。天然源NMVOCs排放会干扰人为源NMVOCs和HCHO柱浓度的关系，实验结合全球大气研究排放数据库（the Emissions Database for Global Atmospheric Research，EDGAR）、火灾排放数据库（Global Fire Emissions Database，GFED）等多源数据识别并排除天然源NMVOCs排放，然后得到一套中国的人为源NMVOCs排放主导的城市站点。城市站点的季节HCHO柱浓度特征一般表现为夏高冬低，平均值分别为1.17×10¹⁶ molecules（molec.） cm^-2，1.02×10¹⁶ molec. cm^-2。中国城市站点的人为源NMVOCs排放（EDGAR估计的HCHO排放速率）和夏季HCHO柱浓度的关系密切（r = 0.91），但与冬季HCHO柱浓度不存在显著的线性关系。分析发现，夏季TROPOMI HCHO柱浓度可指示人为源NMVOCs排放水平。

全球城市区域的人为源NMVOCs排放特征分析。通过全球的TROPOMI HCHO柱浓度（人为源NMVOCs排放指标）和VIIRS夜间灯光辐射（城市化水平指标），建立了人为源NMVOCs排放和城市化水平的线性模型，并进行了敏感性分析。线性模型的相关系数和斜率反映了城市化水平和人为源NMVOCs排放的关系强度，截距反映了城市的背景HCHO柱浓度。选取的全球城市站点的线性模型的相关系数为0.81，斜率为0.42×10¹⁵ molec. cm^-2 nanoWatts^-1 cm² sr，截距为9.26×10¹⁵ molec. cm^-2。在全球、大洲、国家等多个尺度上都发现了人为源NMVOCs排放与城市化水平显著的正相关关系。随后基于陆地生态分区评估了生物源NMVOCs排放对于模型的影响，发现生物源NMVOCs排放影响了模型的相关系数，但一般小于0.20；基于戈达登地球观测系统的大气化学模型（Goddard Earth Observing System Chemistry Model，GEOS-Chem）和TROPOMI NO₂柱浓度等数据评估了NO_x排放对于模型的影响，发现NO_x排放水平不为0时，对模型的斜率影响有限，变化率一般小于30%。

[1] 徐冠华, 柳钦火, 陈良富, 等. 遥感与中国可持续发展:机遇和挑战 [J]. 遥感学报, 2016, 20(05): 679-688.
[2] DING L, ZHAO W T, HUANG Y L, et al. Research on the coupling coordination relationship between urbanization and the air environment: a case study of the area of Wuhan [J]. Atmosphere, 2015, 6(10): 1539-1558.
[3] LARKIN A, VAN DONKELAAR A, GEDDES J A, et al. Relationships between changes in urban characteristics and air quality in East Asia from 2000 to 2010 [J]. Environmental Science Technology, 2016, 50(17): 9142-9149.
[4] LI Y, SCHUBERT S, KROPP J P, et al. On the influence of density and morphology on the urban heat island intensity [J]. Nature Communications, 2020, 11(1): 2647.
[5] DAELLENBACH K R, UZU G, JIANG J, et al. Sources of particulate-matter air pollution and its oxidative potential in Europe [J]. Nature, 2020, 587(7834): 414-419.
[6] EDWARDS P M, BROWN S S, ROBERTS J M, et al. High winter ozone pollution from carbonyl photolysis in an oil and gas basin [J]. Nature, 2014, 514(7522): 351.
[7] 陈良富, 王雅鹏, 张欣欣, 等. 面向区域二次污染风险控制的臭氧及其前体物卫星遥感监测 [J]. 环境监控与预警, 2019, 11(05): 13-21.
[8] FENG Z, KOBAYASHI K. Assessing the impacts of current and future concentrations of surface ozone on crop yield with meta-analysis [J]. Atmospheric Environment, 2009, 43(8): 1510-1519.
[9] FENG Z, KOBAYASHI K, AINSWORTH E A. Impact of elevated ozone concentration on growth, physiology, and yield of wheat: a meta-analysis [J]. Global Change Biology, 2008, 14(11): 2696-2708.
[10] 赵冉, 胡启后, 孙中平, 等. 天地一体化遥感监测大气污染技术进展 [J]. 环境科学研究, 2021, 34(01): 28-40.
[11] WANG T, XUE L K, BRIMBLECOMBE P, et al. Ozone pollution in China: a review of concentrations, meteorological influences, chemical precursors, and effects [J]. Science of the Total Environment, 2017, 575: 1582-1596.
[12] WANG W N, CHENG T H, GU X F, et al. Assessing spatial and temporal patterns of observed ground-level ozone in China [J]. Scientific Reports, 2017, 7: 1122-1139.
[13] 卓俊玲, 朱珊娴, 隆重, 等. 基于卫星数据识别臭氧生成高值区的方法及其应用 [J]. 环境工程技术学报, 2022, 12(06): 2039-2048.
[14] HEGGLIN M I, SHEPHERD T G. Large climate-induced changes in ultraviolet index and stratosphere-to-troposphere ozone flux [J]. Nature Geoscience, 2009, 2(10): 687-691.
[15] KIM S, KIM S Y, LEE M, et al. Impact of isoprene and HONO chemistry on ozone and OVOC formation in a semirural South Korean forest [J]. Atmospheric Chemistry and Physics, 2015, 15(8): 4357-4371.
[16] LI K, JACOB D J, LIAO H, et al. Ozone pollution in the North China Plain spreading into the late-winter haze season [J]. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(10): 566-578.
[17] SOURI A H, NOWLAN C R, WOLFE G M, et al. Revisiting the effectiveness of HCHO/NO2 ratios for inferring ozone sensitivity to its precursors using high resolution airborne remote sensing observations in a high ozone episode during the KORUS-AQ campaign [J]. Atmospheric Environment, 2020, 224: 117341-117363.
[18] BIAN J C, PAN L L, PAULIK L, et al. In situ water vapor and ozone measurements in Lhasa and Kunming during the Asian summer monsoon [J]. Geophysical Research Letters, 2012, 39: 3462-3468.
[19] GOLDBERG D L, HARKEY M, DE FOY B, et al. Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO [J]. Atmospheric Chemistry and Physics, 2022, 22(16): 10875-10900.
[20] JIN X, FIORE A M, MURRAY L T, et al. Evaluating a space‐based indicator of surface ozone-NOx-VOC sensitivity over midlatitude source regions and application to decadal trends [J]. Journal of Geophysical Research: Atmospheres, 2017, 122(19): 101-119.
[21] WANG Y P, YU C, TAO J H, et al. Spatio-temporal characteristics of tropospheric ozone and its precursors in Guangxi, South China [J]. Atmosphere, 2018, 9(9): 231-245.
[22] XUE R, WANG S, ZHANG S, et al. Ozone pollution of megacity Shanghai during city-wide lockdown assessed using TROPOMI observations NO2 and HCHO [J]. Remote Sensing, 2022, 14(24): 945-963.
[23] 陈智海, 杨显玉, 古珊, 等. 基于OMI数据研究中国对流层甲醛时空分布特征及变化趋势 [J]. 环境科学学报, 2019, 39(09): 2852-2859.
[24] 王雅鹏, 陶金花, 余超, 等. 基于卫星遥感的甲醛和乙二醛监测与应用综述 [J]. 三峡生态环境监测, 2020, 5(03): 43-52.
[25] VEEFKIND J P, DE HAAN J R, BRINKSMA E J, et al. Total ozone from the Ozone Monitoring Instrument (OMI) using the DOAS technique [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(5): 1239-1244.
[26] WITTROCK F, RICHTER A, OETJEN H, et al. Simultaneous global observations of glyoxal and formaldehyde from space [J]. Geophysical Research Letters, 2006, 33(16): 8893-8901.
[27] KROTKOV N A, LAMSAL L N, CELARIER E A, et al. The version 3 OMI NO2 standard product [J]. Atmospheric Measurement Techniques, 2017, 10(9): 3133-3149.
[28] LAMSAL L N, KROTKOV N A, CELARIER E A, et al. Evaluation of OMI operational standard NO2 column retrievals using in situ and surface-based NO2 observations [J]. Atmospheric Chemistry and Physics, 2014, 14(21): 11587-11609.
[29] LEE C J, BROOK J R, EVANS G J, et al. Novel application of satellite and in-situ measurements to map surface-level NO2 in the Great Lakes region [J]. Atmospheric Chemistry and Physics, 2011, 11(22): 11761-11775.
[30] LEI R, FENG S, XU Y, et al. Reconciliation of asynchronous satellite-based NO2 and CO2 enhancements with mesoscale modeling over two urban landscapes [J]. Remote Sensing of Environment, 2022, 281: 113241.
[31] ABAD G G, VASILKOV A, SEFTOR C, et al. Smithsonian Astrophysical Observatory Ozone Mapping and Profiler Suite (SAO OMPS) formaldehyde retrieval [J]. Atmospheric Measurement Techniques, 2016, 9(7): 2797-2812.
[32] BARKLEY M P, SMEDT I D, VAN ROOZENDAEL M, et al. Top-down isoprene emissions over tropical South America inferred from SCIAMACHY and OMI formaldehyde columns [J]. Journal of Geophysical Research: Atmospheres, 2013, 118(12): 6849-6868.
[33] FREITAS A D, FORNARO A. Atmospheric formaldehyde monitored by TROPOMI satellite instrument throughout 2020 over São Paulo State, Brazil [J]. Remote Sensing, 2022, 14(13): 3032.
[34] FU T M, JACOB D J, PALMER P I, et al. Space-based formaldehyde measurements as constraints on volatile organic compound emissions in east and south Asia and implications for ozone [J]. Journal of Geophysical Research-Atmospheres, 2007, 112(D6): 15-28.
[35] GUAN J, JIN B, DING Y, et al. Global surface HCHO distribution derived from satellite observations with neural networks technique [J]. Remote Sensing, 2021, 13(20): 4055.
[36] ZHU L, JACOB D J, KEUTSCH F N, et al. Formaldehyde (HCHO) as a hazardous air pollutant: mapping surface air concentrations from satellite and inferring cancer risks in the United States [J]. Environmental Science & Technology, 2017, 51(10): 5650-5657.
[37] CHO C, CLAIR J M S, LIAO J, et al. Evolution of formaldehyde (HCHO) in a plume originating from a petrochemical industry and its volatile organic compounds (VOCs) emission rate estimation [J]. Elementa: Science of the Anthropocene, 2021, 9(1): 123-136.
[38] 王岚新, 晏洋洋, 尹沙沙, 等. 基于卫星遥感的河南省甲醛变化趋势及影响因素分析 [J]. 环境化学, 2023, 42(10): 3449-3460.
[39] BOERSMA K F, ESKES H J, DIRKSEN R J, et al. An improved tropospheric NO2 column retrieval algorithm for the Ozone Monitoring Instrument [J]. Atmospheric Measurement Techniques, 2011, 4(9): 1905-1928.
[40] BOERSMA K F, ESKES H J, RICHTER A, et al. Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project [J]. Atmospheric Measurement Techniques, 2018, 11(12): 6651-6678.
[41] NOWLAN C R, LIU X, CHANCE K, et al. Retrievals of sulfur dioxide from the Global Ozone Monitoring Experiment 2 (GOME-2) using an optimal estimation approach: Algorithm and initial validation [J]. Journal of Geophysical Research, 2011, 116(D18): 1212-1228.
[42] MARTIN R V. Evaluation of GOME satellite measurements of tropospheric NO2 and HCHO using regional data from aircraft campaigns in the southeastern United States [J]. Journal of Geophysical Research, 2004, 109(D24): 7856-7867.
[43] 朱松岩, 余超, 李小英, 等. 紫外大气甲醛卫星遥感反演方法和研究现状 [J]. 中国环境科学, 2018, 38(05): 1685-1694.
[44] MILLER C C, ABAD G G, WANG H, et al. Glyoxal retrieval from the Ozone Monitoring Instrument [J]. Atmospheric Measurement Techniques, 2014, 7(11): 3891-3907.
[45] MILLER C C, JACOB D J, MARAIS E A, et al. Glyoxal yield from isoprene oxidation and relation to formaldehyde: chemical mechanism, constraints from SENEX aircraft observations, and interpretation of OMI satellite data [J]. Atmospheric Chemistry and Physics, 2017, 17(14): 8725-8738.
[46] 何国金, 王力哲, 马艳, 等. 对地观测大数据处理:挑战与思考 [J]. 科学通报, 2015, 60(Z1): 470-478.
[47] 李国庆, 张红月, 张连翀, 等. 地球观测数据共享的发展和趋势 [J]. 遥感学报, 2016, 20(05): 979-990.
[48] 王戈飞, 张佩云, 梁枥文, 等. 地理信息系统与大数据的耦合应用 [J]. 遥感信息, 2017, 32(04): 146-151.
[49] 何国金, 王桂周, 龙腾飞, 等. 对地观测大数据开放共享:挑战与思考 [J]. 中国科学院院刊, 2018, 33(08): 783-790.
[50] 王卷乐, 程凯, 边玲玲, 等. 面向SDGs和美丽中国评价的地球大数据集成框架与关键技术 [J]. 遥感技术与应用, 2018, 33(05): 775-783.
[51] VEEFKIND J P, ABEN I, MCMULLAN K, et al. TROPOMI on the ESA Sentinel-5 Precursor: a GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications [J]. Remote Sensing of Environment, 2012, 120: 70-83.
[52] MOROZOVA A E, SIZOV O S, ELAGIN P O, et al. Integral assessment of atmospheric air quality in the largest cities of Russia based on TROPOMI (Sentinel-5P) data for 2019–2020 [J]. Cosmic Research, 2023, 60(S1): 57-68.
[53] LEVELT P F, STEIN ZWEERS D C, ABEN I, et al. Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI [J]. Atmospheric Chemistry and Physics, 2022, 22(15): 10319-10351.
[54] DE SMEDT I, THEYS N, YU H, et al. Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project [J]. Atmospheric Measurement Techniques, 2018, 11(4): 2395-2426.
[55] GRIFFIN D, MCLINDEN C A, BOERSMA F, et al. High resolution mapping of nitrogen dioxide with TROPOMI: first results and validation over the Canadian oil sands [J]. Geophysical Research Letters, 2019, 46(2): 1049-1060.
[56] CHAN K L, WIEGNER M, VAN GEFFEN J, et al. MAX-DOAS measurements of tropospheric NO2 and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations [J]. Atmospheric Measurement Techniques, 2020, 13(8): 4499-4520.
[57] RYAN R G, SILVER J D, QUEREL R, et al. Comparison of formaldehyde tropospheric columns in Australia and New Zealand using MAX-DOAS, FTIR and TROPOMI [J]. Atmospheric Measurement Techniques, 2020, 13(12): 6501-6519.
[58] VAN GEFFEN J, BOERSMA K F, ESKESL H, et al. S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI [J]. Atmospheric Measurement Techniques, 2020, 13(3): 1315-1335.
[59] CHOI Y, KIM H, TONG D, et al. Summertime weekly cycles of observed and modeled NOx and O3 concentrations as a function of satellite-derived ozone production sensitivity and land use types over the Continental United States [J]. Atmospheric Chemistry and Physics, 2012, 12(14): 6291-6307.
[60] DUNCAN B N, YOSHIDA Y, OLSON J R, et al. Application of OMI observations to a space-based indicator of NOx and VOC controls on surface ozone formation [J]. Atmospheric Environment, 2010, 44(18): 2213-2223.
[61] LIN J T, LIU M Y, XIN J Y, et al. Influence of aerosols and surface reflectance on satellite NO2 retrieval: seasonal and spatial characteristics and implications for NOx emission constraints [J]. Atmospheric Chemistry and Physics, 2015, 15(19): 11217-11241.
[62] ANENBERG S C, HENZE D K, TINNEY V, et al. Estimates of the global burden of ambient PM2.5, ozone, and NO2 on asthma incidence and emergency room visits [J]. Environmental Health Perspectives, 2018, 126(10): 107004.
[63] DAVID L M, NAIR P R. Tropospheric column O3 and NO2 over the Indian region observed by Ozone Monitoring Instrument (OMI): seasonal changes and long-term trends [J]. Atmospheric Environment, 2013, 65: 25-39.
[64] LAMSAL L N, MARTIN R V, PARRISH D D, et al. Scaling relationship for NO2 pollution and urban population size: a satellite perspective [J]. Environmental Science Technology, 2013, 47(14): 7855-7861.
[65] ZHANG L S, LEE C S, ZHANG R Q, et al. Spatial and temporal evaluation of long term trend (2005-2014) of OMI retrieved NO2 and SO2 concentrations in Henan Province, China [J]. Atmospheric Environment, 2017, 154: 151-166.
[66] GEORGOULIAS A K, VAN DER A R J, STAMMES P, et al. Trends and trend reversal detection in 2 decades of tropospheric NO2 satellite observations [J]. Atmospheric Chemistry and Physics, 2019, 19(9): 6269-6294.
[67] MILLET D B, JACOB D J, BOERSMA K F, et al. Spatial distribution of isoprene emissions from North America derived from formaldehyde column measurements by the OMI satellite sensor [J]. Journal of Geophysical Research-Atmospheres, 2008, 113(D2): 98-109.
[68] SHIM C, WANG Y H, CHOI Y, et al. Constraining global isoprene emissions with Global Ozone Monitoring Experiment (GOME) formaldehyde column measurements [J]. Journal of Geophysical Research-Atmospheres, 2005, 110(D24): 1698-1709.
[69] PALMER P I, ABBOT D S, FU T M, et al. Quantifying the seasonal and interannual variability of North American isoprene emissions using satellite observations of the formaldehyde column [J]. Journal of Geophysical Research-Atmospheres, 2006, 111(D12): 983-996.
[70] ZHU L, MICKLEY L J, JACOB D J, et al. Long-term (2005-2014) trends in formaldehyde (HCHO) columns across North America as seen by the OMI satellite instrument: Evidence of changing emissions of volatile organic compounds [J]. Geophysical Research Letters, 2017, 44(13): 7079-7086.
[71] DUAN J, TAN J, YANG L, et al. Concentration, sources and ozone formation potential of volatile organic compounds (VOCs) during ozone episode in Beijing [J]. Atmospheric Research, 2008, 88(1): 25-35.
[72] MARAIS E A, JACOB D J, KUROSU T P, et al. Isoprene emissions in Africa inferred from OMI observations of formaldehyde columns [J]. Atmospheric Chemistry and Physics, 2012, 12(14): 6219-6235.
[73] CHANCE K, PALMER P I, SPURR R J D, et al. Satellite observations of formaldehyde over North America from GOME [J]. Geophysical Research Letters, 2000, 27(21): 3461-3464.
[74] PALMER P I, JACOB D J, CHANCE K, et al. Air mass factor formulation for spectroscopic measurements from satellites: application to formaldehyde retrievals from the Global Ozone Monitoring Experiment [J]. Journal of Geophysical Research: Atmospheres, 2001, 106(D13): 14539-14550.
[75] MILLET D B, JACOB D J, TURQUETY S, et al. Formaldehyde distribution over North America: implications for satellite retrievals of formaldehyde columns and isoprene emission [J]. Journal of Geophysical Research-Atmospheres, 2006, 111(D24): 635-646.
[76] FRIED A, WALEGA J G, OLSON J R, et al. Formaldehyde over North America and the North Atlantic during the summer 2004 INTEX campaign: Methods, observed distributions, and measurement-model comparisons [J]. Journal of Geophysical Research-Atmospheres, 2008, 113(D10): 2356-2369.
[77] ABBOT D S, PALMER P I, MARTIN R V, et al. Seasonal and interannual variability of North American isoprene emissions as determined by formaldehyde column measurements from space [J]. Geophysical Research Letters, 2003, 30(17): 322-329.
[78] DUNCAN B N, YOSHIDA Y, DAMON M R, et al. Temperature dependence of factors controlling isoprene emissions [J]. Geophysical Research Letters, 2009, 36: 12-20.
[79] JBAILY A, ZHOU X, LIU J, et al. Air pollution exposure disparities across US population and income groups [J]. Nature, 2022, 601(7892): 228-233.
[80] KAMPA M, CASTANAS E. Human health effects of air pollution [J]. Environmental Pollution, 2008, 151(2): 362-367.
[81] HYSTAD P, SETTON E, CERVANTES A, et al. Creating national air pollution models for population exposure assessment in Canada [J]. Environmental Health Perspectives, 2011, 119(8): 1123-1129.
[82] DIGANGI J P, BOYLE E S, KARL T, et al. First direct measurements of formaldehyde flux via eddy covariance: implications for missing in-canopy formaldehyde sources [J]. Atmospheric Chemistry and Physics, 2011, 11(20): 10565-10578.
[83] LIAO J, HANISCO T F, WOLFE G M, et al. Towards a satellite formaldehyde – in situ hybrid estimate for organic aerosol abundance [J]. Atmospheric Chemistry and Physics, 2019, 19(5): 2765-2785.
[84] BAUWENS M, STAVRAKOU T, MüLLER J-F, et al. Nine years of global hydrocarbon emissions based on source inversion of OMI formaldehyde observations [J]. Atmospheric Chemistry and Physics, 2016, 16(15): 10133-10158.
[85] CHEN Y, LIU C, SU W, et al. Identification of volatile organic compound emissions from anthropogenic and biogenic sources based on satellite observation of formaldehyde and glyoxal [J]. Science of the Total Environment, 2023, 859(1): 159997.
[86] GREEN J R, FIDDLER M N, FIBIGER D L, et al. Wintertime formaldehyde: airborne observations and source apportionment over the eastern United States [J]. Journal of Geophysical Research: Atmospheres, 2021, 126(5): 75-89.
[87] CURCI G, PALMER P I, KUROSU T P, et al. Estimating European volatile organic compound emissions using satellite observations of formaldehyde from the Ozone Monitoring Instrument [J]. Atmospheric Chemistry and Physics, 2010, 10(23): 11501-11517.
[88] MCDUFFIE E E, SMITH S J, O'ROURKE P, et al. A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970-2017): an application of the Community Emissions Data System (CEDS) [J]. Earth System Science Data, 2020, 12(4): 3413-3442.
[89] CAO H S, FU T M, ZHANG L, et al. Adjoint inversion of Chinese non-methane volatile organic compound emissions using space-based observations of formaldehyde and glyoxal [J]. Atmospheric Chemistry and Physics, 2018, 18(20): 15017-15046.
[90] TIAN W, CHIPPERFIELD M P. A new coupled chemistry–climate model for the stratosphere: The importance of coupling for future O3‐climate predictions [J]. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 2005, 131(605): 281-303.
[91] LAMSAL L N, MARTIN R V, VAN DONKELAAR A, et al. Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument [J]. Journal of Geophysical Research-Atmospheres, 2008, 113(D16): 456-469.
[92] HEWITT C N, ASHWORTH K, BOYNARD A, et al. Ground-level ozone influenced by circadian control of isoprene emissions [J]. Nature Geoscience, 2011, 4(10): 671-674.
[93] WOLFE G M, THORNTON J A, MCKAY M, et al. Forest-atmosphere exchange of ozone: sensitivity to very reactive biogenic VOC emissions and implications for in-canopy photochemistry [J]. Atmospheric Chemistry and Physics, 2011, 11(15): 7875-7891.
[94] XU J, MA J Z, ZHANG X L, et al. Measurements of ozone and its precursors in Beijing during summertime: impact of urban plumes on ozone pollution in downwind rural areas [J]. Atmospheric Chemistry and Physics, 2011, 11(23): 12241-12252.
[95] ZHOU D R, DING A J, MAO H T, et al. Impacts of the East Asian monsoon on lower tropospheric ozone over coastal South China [J]. Environmental Research Letters, 2013, 8(4): 23-31.
[96] CHOI Y, SOURI A H. Seasonal behavior and long-term trends of tropospheric ozone, its precursors and chemical conditions over Iran: A view from space [J]. Atmospheric Environment, 2015, 106: 232-240.
[97] WOMACK C C, MCDUFFIE E E, EDWARDS P M, et al. An odd oxygen framework for wintertime ammonium nitrate aerosol pollution in urban areas: NOx and VOC control as mitigation strategies [J]. Geophysical Research Letters, 2019, 46(9): 4971-4979.
[98] SOURI A H, JOHNSON M S, WOLFE G M, et al. Characterization of errors in satellite-based HCHO ∕ NO2 tropospheric column ratios with respect to chemistry, column-to-PBL translation, spatial representation, and retrieval uncertainties [J]. Atmospheric Chemistry and Physics, 2023, 23(3): 1963-1986.
[99] FU D, MILLET D B, WELLS K C, et al. Direct retrieval of isoprene from satellite-based infrared measurements [J]. Nature Communications, 2019, 10(1): 3811.
[100] TISZENKEL L, LEE S H. Synergetic effects of isoprene and HOx on biogenic new particle formation [J]. Geophysical Research Letters, 2023, 50(14): 5333-5340.
[101] LIAO J, WOLFE G M, HANNUN R A, et al. Formaldehyde evolution in US wildfire plumes during the Fire Influence on Regional to Global Environments and Air Quality experiment (FIREX-AQ) [J]. Atmospheric Chemistry and Physics, 2021, 21(24): 18319-18331.
[102] HONG Q, ZHU L, XING C, et al. Inferring vertical variability and diurnal evolution of O3 formation sensitivity based on the vertical distribution of summertime HCHO and NO2 in Guangzhou, China [J]. Science of the Total Environment, 2022, 827: 154045.
[103] CHOI S, LAMSAL L N, FOLLETTE-COOK M, et al. Assessment of NO2 observations during DISCOVER-AQ and KORUS-AQ field campaigns [J]. Atmospheric Measurement Techniques, 2020, 13(5): 2523-2546.
[104] LIU M Y, LIN J T, BOERSMA K F, et al. Improved aerosol correction for OMI tropospheric NO2 retrieval over East Asia: constraint from CALIOP aerosol vertical profile [J]. Atmospheric Measurement Techniques, 2019, 12(1): 1-21.
[105] LI M, KLIMONT Z, ZHANG Q, et al. Comparison and evaluation of anthropogenic emissions of SO2 and NOx over China [J]. Atmospheric Chemistry and Physics, 2018, 18(5): 3433-3456.
[106] FIOLETOV V, MCLINDEN C A, KHAROL S K, et al. Multi-source SO2 emission retrievals and consistency of satellite and surface measurements with reported emissions [J]. Atmospheric Chemistry and Physics, 2017, 17(20): 12597-12616.
[107] BUTLAND B K, ANDERSON H R, VAN DONKELAAR A, et al. Ambient air pollution and the prevalence of rhinoconjunctivitis in adolescents: a worldwide ecological analysis [J]. Air Quality Atmosphere Health, 2018, 11(7): 755-764.
[108] CHANCE K, LIU X, MILLER C C, et al. TEMPO Green Paper: chemistry, physics, and meteorology experiments with the tropospheric emissions: monitoring of pollution instrument [Z]. Sensors, Systems, and Next-Generation Satellites. 2019(1): 89-124
[109] LU Z, STREETS D G, DE FOY B, et al. Ozone monitoring instrument observations of interannual increases in SO2 emissions from Indian coal-fired power plants during 2005-2012 [J]. Environmental Science Technology, 2013, 47(24): 13993-14000.
[110] MENG J, MARTIN R V, LI C, et al. Source contributions to ambient fine particulate matter for Canada [J]. Environmental Science Technology, 2019, 53(17): 10269-10278.
[111] CARN S A, FIOLETOV V E, MCLINDEN C A, et al. A decade of global volcanic SO2 emissions measured from space [J]. Scientific Reports, 2017, 7: 102-116.
[112] GIGLIO L, RANDERSON J T, VAN DER WERF G R. Analysis of daily, monthly, and annual burned area using the fourth‐generation global fire emissions database (GFED4) [J]. Journal of Geophysical Research: Biogeosciences, 2013, 118(1): 317-328.
[113] WENG H, LIN J, MARTIN R, et al. Global high-resolution emissions of soil NOx, sea salt aerosols, and biogenic volatile organic compounds [J]. Scientific Data, 2020, 7(1): 148.
[114] MENG L, GRAUS W, WORRELL E, et al. Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a case study for China [J]. Energy, 2014, 71: 468-478.
[115] SHU L, ZHU L, BAK J, et al. Improved ozone simulation in East Asia via assimilating observations from the first geostationary air-quality monitoring satellite: Insights from an Observing System Simulation Experiment [J]. Atmospheric Environment, 2022, 274: 119003-119026.
[116] COOPER M J, MARTIN R V, HENZE D K, et al. Effects of a priori profile shape assumptions on comparisons between satellite NO2 columns and model simulations [J]. Atmospheric Chemistry and Physics, 2020, 20(12): 7231-7241.
[117] ANDERSON D C, DUNCAN B N, NICELY J M, et al. Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy [J]. Atmospheric Chemistry and Physics, 2023, 23(11): 6319-6338.
[118] KHAROL S K, MARTIN R V, PHILIP S, et al. Assessment of the magnitude and recent trends in satellite-derived ground-level nitrogen dioxide over North America [J]. Atmospheric Environment, 2015, 118: 236-245.
[119] MCLINDEN C A, FIOLETOV V, BOERSMA K F, et al. Improved satellite retrievals of NO2 and SO2 over the Canadian oil sands and comparisons with surface measurements [J]. Atmospheric Chemistry and Physics, 2014, 14(7): 3637-3656.
[120] LAMSAL L N, KROTKOV N A, VASILKOV A, et al. Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments [J]. Atmospheric Measurement Techniques, 2021, 14(1): 455-479.
[121] GEDDES J A, MARTIN R V, BUCSELA E J, et al. Stratosphere–troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument [J]. Atmospheric Measurement Techniques, 2018, 11(11): 6271-6287.
[122] BOEKE N L, MARSHALL J D, ALVAREZ S, et al. Formaldehyde columns from the Ozone Monitoring Instrument: urban versus background levels and evaluation using aircraft data and a global model [J]. Journal of Geophysical Research, 2011, 116(D5): 12-25.
[123] LIN Y C, SCHWAB J J, DEMERJIAN K L, et al. Summertime formaldehyde observations in New York City: Ambient levels, sources and its contribution to HOx radicals [J]. Journal of Geophysical Research: Atmospheres, 2012, 117(D8): 5232-5241.
[124] SURL L, PALMER P I, ABAD G G. Which processes drive observed variations of HCHO columns over India? [J]. Atmospheric Chemistry and Physics, 2018, 18(7): 4549-4566.
[125] LEE C, MARTIN R V, VAN DONKELAAR A, et al. Retrieval of vertical columns of sulfur dioxide from SCIAMACHY and OMI: Air mass factor algorithm development, validation, and error analysis [J]. Journal of Geophysical Research, 2009, 114(D22): 963-975.
[126] DUFOUR G, EREMENKO M, CUESTA J, et al. Springtime daily variations in lower-tropospheric ozone over east Asia: the role of cyclonic activity and pollution as observed from space with IASI [J]. Atmospheric Chemistry and Physics, 2015, 15(18): 10839-10856.
[127] MILNE B T. Multiscale assessment of binary and continuous land cover variables for MODIS validation, mapping, and modeling applications [J]. Remote Sensing of Environment, 1999: 1235-1251.
[128] LUNETTA R S, KNIGHT J F, EDIRIWICKREMA J, et al. Land-cover change detection using multi-temporal MODIS NDVI data [J]. Remote Sensing of Environment, 2006, 105(2): 142-154.
[129] KWON H A, ABAD G G, NOWLAN C R, et al. Validation of OMPS Suomi NPP and OMPS NOAA-20 formaldehyde total columns with NDACC FTIR observations [J]. Earth and Space Science, 2023, 10(5): 2-18.
[130] GONZáLEZ ABAD G, VASILKOV A, SEFTOR C, et al. Smithsonian Astrophysical Observatory Ozone Mapping and Profiler Suite (SAO OMPS) formaldehyde retrieval [J]. Atmospheric Measurement Techniques, 2016, 9(7): 2797-2812.
[131] JOHNSON M S, SOURI A H, PHILIP S, et al. Satellite remote-sensing capability to assess tropospheric-column ratios of formaldehyde and nitrogen dioxide: case study during the Long Island Sound Tropospheric Ozone Study 2018 (LISTOS 2018) field campaign [J]. Atmospheric Measurement Techniques, 2023, 16(9): 2431-2454.
[132] LEE H, RYU J, IRIE H, et al. Investigations of the diurnal variation of vertical HCHO profiles based on MAX-DOAS measurements in Beijing: comparisons with OMI vertical column data [J]. Atmosphere, 2015, 6(11): 1816-1832.
[133] LI C, JOINER J, KROTKOV N A, et al. A new method for global retrievals of HCHO total columns from the Suomi National Polar-orbiting Partnership Ozone Mapping and Profiler Suite [J]. Geophysical Research Letters, 2015, 42(7): 2515-2522.
[134] SHEN L, JACOB D J, ZHU L, et al. The 2005–2016 trends of formaldehyde columns over China observed by satellites: increasing anthropogenic emissions of volatile organic compounds and decreasing agricultural fire emissions [J]. Geophysical Research Letters, 2019, 46(8): 4468-4475.
[135] FIOLETOV V E, MCLINDEN C A, KROTKOV N, et al. Application of OMI, SCIAMACHY, and GOME-2 satellite SO2 retrievals for detection of large emission sources [J]. Journal of Geophysical Research: Atmospheres, 2013, 118(19): 11,399-311,418.
[136] DE SMEDT I, MULLER J F, STAVRAKOU T, et al. Twelve years of global observations of formaldehyde in the troposphere using GOME and SCIAMACHY sensors [J]. Atmospheric Chemistry and Physics, 2008, 8(16): 4947-4963.
[137] STAVRAKOU T, MüLLER J F, BAUWENS M, et al. How consistent are top-down hydrocarbon emissions based on formaldehyde observations from GOME-2 and OMI? [J]. Atmospheric Chemistry and Physics, 2015, 15(20): 11861-11884.
[138] WANG Y, BEIRLE S, LAMPEL J, et al. Validation of OMI, GOME-2A and GOME-2B tropospheric NO2, SO2 and HCHO products using MAX-DOAS observations from 2011 to 2014 in Wuxi, China: investigation of the effects of priori profiles and aerosols on the satellite products [J]. Atmospheric Chemistry and Physics, 2017, 17(8): 5007-5033.
[139] ZARA M, BOERSMA K F, DE SMEDT I, et al. Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends [J]. Atmospheric Measurement Techniques, 2018, 11(7): 4033-4058.
[140] DE SMEDT I, STAVRAKOU T, HENDRICK F, et al. Diurnal, seasonal and long-term variations of global formaldehyde columns inferred from combined OMI and GOME-2 observations [J]. Atmospheric Chemistry and Physics, 2015, 15(21): 12519-12545.
[141] CHOI Y, SOURI A H. Chemical condition and surface ozone in large cities of Texas during the last decade: Observational evidence from OMI, CAMS, and model analysis [J]. Remote Sensing of Environment, 2015, 168: 90-101.
[142] GONZALEZ ABAD G, LIU X, CHANCE K, et al. Updated Smithsonian Astrophysical Observatory Ozone Monitoring Instrument (SAO OMI) formaldehyde retrieval [J]. Atmospheric Measurement Techniques, 2015, 8(1): 19-32.
[143] ZHU L, JACOB D J, KIM P S, et al. Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEACRS aircraft observations over the southeast US [J]. Atmospheric Chemistry and Physics, 2016, 16(21): 13477-13490.
[144] ZHANG Y, LI C, KROTKOV N A, et al. Continuation of long-term global SO2 pollution monitoring from OMI to OMPS [J]. Atmospheric Measurement Techniques, 2017, 10(4): 1213-1235.
[145] VIGOUROUX C, LANGEROCK B, AQUINO C A B, et al. TROPOMI-Sentinel-5 Precursor formaldehyde validation using an extensive network of ground-based Fourier-transform infrared stations [J]. Atmospheric Measurement Techniques, 2020, 13(7): 3751-3767.
[146] COOPER M J, MARTIN R V, MCLINDEN C A, et al. Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument [J]. Environmental Research Letters, 2020, 15(10): 104013-104019.
[147] KAISER J, JACOB D J, ZHU L, et al. High-resolution inversion of OMI formaldehyde columns to quantify isoprene emission on ecosystem-relevant scales: application to the southeast US [J]. Atmospheric Chemistry and Physics, 2018, 18(8): 5483-5497.
[148] JIN X M, HOLLOWAY T. Spatial and temporal variability of ozone sensitivity over China observed from the Ozone Monitoring Instrument [J]. Journal of Geophysical Research-Atmospheres, 2015, 120(14): 7229-7246.
[149] SHU L, ZHU L, BAK J, et al. Improving ozone simulations in Asia via multisource data assimilation: results from an observing system simulation experiment with GEMS geostationary satellite observations [J]. Atmospheric Chemistry and Physics, 2023, 23(6): 3731-3748.
[150] ABAD G G, LIU X, CHANCE K, et al. Updated Smithsonian Astrophysical Observatory Ozone Monitoring Instrument (SAO OMI) formaldehyde retrieval [J]. Atmospheric Measurement Techniques, 2015, 8(1): 19-32.
[151] SUN W F, ZHU L, DE SMEDT I, et al. Global Significant Changes in Formaldehyde (HCHO) Columns Observed From Space at the Early Stage of the COVID-19 Pandemic [J]. Geophysical Research Letters, 2021, 48(4): 233-240.
[152] DE SMEDT I, PINARDI G, VIGOUROUX C, et al. Comparative assessment of TROPOMI and OMI formaldehyde observations and validation against MAX-DOAS network column measurements [J]. Atmospheric Chemistry and Physics, 2021, 21(16): 12561-12593.
[153] VLEMMIX T, HENDRICK F, PINARDI G, et al. MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches [J]. Atmospheric Measurement Techniques, 2015, 8(2): 941-963.
[154] ZHAO Y, MO X, WANG H, et al. A comparative study of ground-gridded and satellite-derived formaldehyde during ozone episodes in the Chinese Greater Bay Area [J]. Remote Sensing, 2023, 15(16): 3998-4012.
[155] ZHU L, ABAD G G, NOWLAN C R, et al. Validation of satellite formaldehyde (HCHO) retrievals using observations from 12 aircraft campaigns [J]. Atmospheric Chemistry and Physics, 2020, 20(20): 12329-12345.
[156] JOHANSSON J K E, MELLQVIST J, SAMUELSSON J, et al. Quantitative measurements and modeling of industrial formaldehyde emissions in the Greater Houston area during campaigns in 2009 and 2011 [J]. Journal of Geophysical Research: Atmospheres, 2014, 119(7): 4303-4322.
[157] WANG Y P, WANG Z F, YU C, et al. Validation of OMI HCHO products using MAX-DOAS observations from 2010 to 2016 in Xianghe, Beijing: investigation of the effects of aerosols on satellite products [J]. Remote Sensing, 2019, 11(2): 129-142.
[158] LORENTE A, BOERSMA K F, YU H, et al. Structural uncertainty in air mass factor calculation for NO2 and HCHO satellite retrievals [J]. Atmospheric Measurement Techniques, 2017, 10(3): 759-782.
[159] ZHU L, JACOB D J, MICKLEY L J, et al. Anthropogenic emissions of highly reactive volatile organic compounds in eastern Texas inferred from oversampling of satellite (OMI) measurements of HCHO columns [J]. Environmental Research Letters, 2014, 9(11): 152-163.
[160] EHN M, THORNTON J A, KLEIST E, et al. A large source of low-volatility secondary organic aerosol [J]. Nature, 2014, 506(7489): 476.
[161] SABER A N, ZHANG H F, CERVANTES-AVILES P, et al. Emerging concerns of VOCs and SVOCs in coking wastewater treatment processes: distribution profile, emission characteristics, and health risk assessment [J]. Environmental Pollution, 2020, 265: 10-23.
[162] HUANG G L, BROOK R, CRIPPA M, et al. Speciation of anthropogenic emissions of non-methane volatile organic compounds: a global gridded data set for 1970-2012 [J]. Atmospheric Chemistry and Physics, 2017, 17(12): 7683-7701.
[163] SOURI A H, NOWLAN C R, ABAD G G, et al. An inversion of NOx and non-methane volatile organic compound (NMVOC) emissions using satellite observations during the KORUS-AQ campaign and implications for surface ozone over East Asia [J]. Atmospheric Chemistry and Physics, 2020, 20(16): 9837-9854.
[164] WEI W, LV Z F, LI Y, et al. A WRF-Chem model study of the impact of VOCs emission of a huge petrochemical industrial zone on the summertime ozone in Beijing, China [J]. Atmospheric Environment, 2018, 175: 44-53.
[165] PAKKATTIL A, MUHSIN M, VARMA M K R. COVID-19 lockdown: effects on selected volatile organic compound (VOC) emissions over the major Indian metro cities [J]. Urban Climate, 2021, 37: 852-867.
[166] XING C Z, LIU C, HU Q H, et al. Identifying the wintertime sources of volatile organic compounds (VOCs) from MAX-DOAS measured formaldehyde and glyoxal in Chongqing, southwest China [J]. Science of the Total Environment, 2020, 715: 234-251.
[167] LEROT C, MüLLER J F, THEYS N, et al. Satellite evidence for glyoxal depletion in elevated fire plumes [J]. Geophysical Research Letters, 2023, 50(4): 162-172.
[168] LIU X, HUEY L G, YOKELSON R J, et al. Airborne measurements of western U.S. wildfire emissions: comparison with prescribed burning and air quality implications [J]. Journal of Geophysical Research: Atmospheres, 2017, 122(11): 6108-6129.
[169] LIU X, ZHANG Y, HUEY L G, et al. Agricultural fires in the southeastern U.S. during SEAC4RS: emissions of trace gases and particles and evolution of ozone, reactive nitrogen, and organic aerosol [J]. Journal of Geophysical Research: Atmospheres, 2016, 121(12): 7383-7414.
[170] NAVARRO K M, WEST M R, O'DELL K, et al. Exposure to particulate matter and estimation of volatile organic compounds across wildland firefighter job tasks [J]. Environmental Science Technology, 2021, 55(17): 11795-11804.
[171] MARTIN R V. Satellite remote sensing of surface air quality [J]. Atmospheric Environment, 2008, 42(34): 7823-7843.
[172] STREETS D G, CANTY T, CARMICHAEL G R, et al. Emissions estimation from satellite retrievals: a review of current capability [J]. Atmospheric Environment, 2013, 77: 1011-1042.
[173] CHOI W, FALOONA I C, BOUVIER-BROWN N C, et al. Observations of elevated formaldehyde over a forest canopy suggest missing sources from rapid oxidation of arboreal hydrocarbons [J]. Atmospheric Chemistry and Physics, 2010, 10(18): 8761-8781.
[174] SU W, HU Q, CHEN Y, et al. Inferring global surface HCHO concentrations from multisource hyperspectral satellites and their application to HCHO-related global cancer burden estimation [J]. Environment International, 2022, 170: 107600.
[175] VALIN L C, FIORE A M, CHANCE K, et al. The role of OH production in interpreting the variability of CH2O columns in the southeast US [J]. Journal of Geophysical Research-Atmospheres, 2016, 121(1): 478-493.
[176] LI J F, WANG Y H, ZHANG R X, et al. Comprehensive evaluations of diurnal NO2 measurements during DISCOVER-AQ 2011: effects of resolution-dependent representation of NOx emissions [J]. Atmospheric Chemistry and Physics, 2021, 21(14): 11133-11160.
[177] WITTE J C, DUNCAN B N, DOUGLASS A R, et al. The unique OMI HCHO/NO2 feature during the 2008 Beijing Olympics: Implications for ozone production sensitivity [J]. Atmospheric Environment, 2011, 45(18): 3103-3111.
[178] YANG L H, JACOB D J, COLOMBI N K, et al. Tropospheric NO2 vertical profiles over South Korea and their relation to oxidant chemistry: implications for geostationary satellite retrievals and the observation of NO2 diurnal variation from space [J]. Atmospheric Chemistry and Physics, 2023, 23(4): 2465-2481.
[179] ZHAN Y, LUO Y Z, DENG X F, et al. Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal kriging model [J]. Environmental Science & Technology, 2018, 52(7): 4180-4189.
[180] BUCSELA E J, KROTKOV N A, CELARIER E A, et al. A new stratospheric and tropospheric NO2 retrieval algorithm for nadir-viewing satellite instruments: applications to OMI [J]. Atmospheric Measurement Techniques, 2013, 6(10): 2607-2626.
[181] GEDDES J A, MARTIN R V, BOYS B L, et al. Long-term trends worldwide in ambient NO2 concentrations inferred from satellite observations [J]. Environmental Health Perspectives, 2016, 124(3): 281-289.
[182] WOLFE G M, KAISER J, HANISCO T F, et al. Formaldehyde production from isoprene oxidation across NOx regimes [J]. Atmospheric Chemistry and Physics, 2016, 16(4): 2597-2610.
[183] GONG P, LIANG S, CARLTON E J, et al. Urbanisation and health in China [J]. The Lancet, 2012, 379(9818): 843-852.
[184] ZHAO L. Urban growth and climate adaptation [J]. Nature Climate Change, 2018: 9562.
[185] CAI C, LI P, JIN H. Extraction of urban impervious surface using two-season WorldView-2 images: a comparison [J]. Photogrammetric Engineering & Remote Sensing, 2016, 82(5): 335-349.
[186] KRAYENHOFF E S. Diurnal interaction between urban expansion,climate change and adaptation in US cities [J]. Nature Climate Change, 2018: 852.
[187] SETO K C, FRAGKIAS M. Quantifying Spatiotemporal Patterns of Urban Land-use Change in Four Cities of China with Time Series Landscape Metrics [J]. Landscape Ecology, 2005, 20(7): 871-888.
[188] LI M, ZANG S, WU C, et al. Spatial and temporal variation of the urban impervious surface and its driving forces in the central city of Harbin [J]. Journal of Geographical Sciences, 2018, 28(3): 323-336.
[189] LI X, ZHOU Y, EOM J, et al. Projecting global urban area growth through 2100 based on historical time series data and future shared socioeconomic pathways [J]. Earths Future, 2019: 26-45.
[190] WENG Q. Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends [J]. Remote Sensing of Environment, 2012, 117: 34-49.
[191] ZHOU D, ZHAO S, ZHANG L, et al. Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities [J]. Remote Sensing of Environment, 2016, 176: 272-281.
[192] BRODY H. Urban health and well-being [J]. Nature, 2016: 9651.
[193] STERN D I. The rise and fall of the environmental Kuznets curve [J]. World Development, 2004, 32(8): 1419-1439.
[194] SINHA A, BHATTACHARYA J. Environmental Kuznets curve estimation for NO2 emission: a case of Indian cities [J]. Ecological Indicators, 2016, 67: 1-11.
[195] LI G D, FANG C L, WANG S J, et al. The effect of economic growth, urbanization, and industrialization on fine particulate matter (PM2.5) concentrations in China [J]. Environmental Science & Technology, 2016, 50(21): 11452-11459.
[196] EASTHAM S D, LONG M S, KELLER C A, et al. GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications [J]. Geoscientific Model Development, 2018, 11(7): 2941-2953.
[197] ZHANG S, WANG S, XUE R, et al. Impact assessment of COVID-19 lockdown on vertical distributions of NO2 and HCHO from MAX-DOAS observations and machine learning models [J]. Journal of Geophysical Research, 2022, 127(15): 233-245.
[198] VANICEK K. Differences between ground Dobson, Brewer and satellite TOMS-8, GOME-WFDOAS total ozone observations at Hradec kralove, Czech [J]. Atmospheric Chemistry and Physics, 2006, 6: 5163-5171.
[199] SMALL C, POZZI F, ELVIDGE C. Spatial analysis of global urban extent from DMSP-OLS night lights [J]. Remote Sensing of Environment, 2005, 96(3-4): 277-291.
[200] DOLL C N H, MULLER J-P, MORLEY J G. Mapping regional economic activity from night-time light satellite imagery [J]. Ecological Economics, 2006, 57(1): 75-92.
[201] LI X, XU H, CHEN X, et al. Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China [J]. Remote Sensing, 2013, 5(6): 3057-3081.
[202] CASTRENCE M, NONG D, TRAN C, et al. Mapping urban transitions using multi-temporal Landsat and DMSP-OLS night-time lights imagery of the Red River Delta in Vietnam [J]. Land, 2014, 3(1): 148-166.
[203] 江威, 何国金, 刘慧婵. NPP/VIIRS和DMSP/OLS夜光数据模拟社会经济参量对比 [J]. 遥感信息, 2016, 31(04): 28-34.
[204] 江威, 何国金, 刘慧婵, 等. 利用DMSP/OLS夜间灯光影像模拟中国经济参量 [J]. 遥感信息, 2018, 33(01): 29-35.
[205] ELVIDGE C D, BAUGH K E, DIETZ J B, et al. Radiance calibration of DMSP-OLS low-light imaging data of human settlements [J]. Remote Sensing of Environment, 1999, 68(1): 77-88.
[206] LE BORGNE P, PéRé S, ROQUET H. Night time detection of Saharan dust using infrared window channels: Application to NPP/VIIRS [J]. Remote Sensing of Environment, 2013, 137: 264-273.
[207] CAO C, BAI Y. Quantitative analysis of VIIRS DNB nightlight point source for light power estimation and stability monitoring [J]. Remote Sensing, 2014, 6(12): 11915-11935.
[208] MA T, ZHOU C, PEI T, et al. Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities [J]. Remote Sensing Letters, 2014, 5(2): 165-174.
[209] WANG J, AEGERTER C, XU X, et al. Potential application of VIIRS Day/Night Band for monitoring nighttime surface PM2.5 air quality from space [J]. Atmospheric Environment, 2016, 124: 55-63.
[210] ZHANG M. Noise reduction and atmospheric correction for coastal applications of Landsat Thematic Mapper imagery [J]. Remote Sensing of Environment, 1999: 12-31.
[211] SMALL C. The Landsat ETM+ spectral mixing space [J]. Remote Sensing of Environment, 2004, 93(1-2): 1-17.
[212] LOZANO F J, SUREZ-SEOANE S, DE LUIS E. Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling [J]. Remote Sensing of Environment, 2007, 107(4): 533-544.
[213] CRISTóBAL J, JIMéNEZ‐MUñOZ J C, SOBRINO J A, et al. Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature [J]. Journal of Geophysical Research, 2009, 114(D8): 2231-2243.
[214] BRANDT J S, KUEMMERLE T, LI H, et al. Using Landsat imagery to map forest change in southwest China in response to the national logging ban and ecotourism development [J]. Remote Sensing of Environment, 2012, 121: 358-369.
[215] ZHU Z, WOODCOCK C E, ROGAN J, et al. Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data [J]. Remote Sensing of Environment, 2012, 117: 72-82.
[216] HOLDEN C E, WOODCOCK C E. An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations [J]. Remote Sensing of Environment, 2016, 185: 16-36.
[217] LU M, APPEL M, PEBESMA E. Multidimensional arrays for analyzing geoscientific data [J]. ISPRS International Journal of Geo-Information, 2018, 7(8): 313-322.
[218] SCHMIDT G. Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm description [M]. USA: USGS, 2013.
[219] MAHDIANPARI M, SALEHI B, MOHAMMADIMANESH F, et al. The first wetland inventory map of Newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform [J]. Remote Sensing, 2018, 11(1): 43.
[220] MATEO-GARCIA G, GOMEZ-CHOVA L, AMOROS-LOPEZ J, et al. Multitemporal cloud masking in the Google Earth Engine [J]. Remote Sensing, 2018, 10(7): 1079.
[221] PARKS S, HOLSINGER L, VOSS M, et al. Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential [J]. Remote Sensing, 2018, 10(6): 879.
[222] GORELICK N, HANCHER M, DIXON M, et al. Google Earth Engine: planetary-scale geospatial analysis for everyone [J]. Remote Sensing of Environment, 2017, 202: 18-27.
[223] SIDHU N, PEBESMA E, CâMARA G. Using Google Earth Engine to detect land cover change: Singapore as a use case [J]. European Journal of Remote Sensing, 2018, 51(1): 486-500.
[224] BREIMAN L. Random forests [J]. Machine Learning, 2001, 45(1): 5-32.
[225] RODRIGUEZ-GALIANO V F, GHIMIRE B, ROGAN J, et al. An assessment of the effectiveness of a random forest classifier for land-cover classification [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 93-104.
[226] PADILLA M, STEHMAN S V, CHUVIECO E. Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling [J]. Remote Sensing of Environment, 2014, 144: 187-196.
[227] PADILLA M, STEHMAN S V, RAMO R, et al. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation [J]. Remote Sensing of Environment, 2015, 160: 114-121.
[228] BELGIU M, DRĂGUŢ L. Random forest in remote sensing: A review of applications and future directions [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114: 24-31.
[229] PAUL A, MUKHERJEE D P, DAS P, et al. Improved random forest for classification [J]. IEEE Transactions on Image Processing, 2018: 59-69.
[230] TYRALIS H, PAPACHARALAMPOUS G, LANGOUSIS A. A brief review of random forests for water scientists and practitioners and their recent history in water resources [J]. Water, 2019, 11(5): 6653-6669.
[231] MASOUMI F, ESLAMKISH T, ABKAR A A, et al. Integration of spectral, thermal, and textural features of ASTER data using Random Forests classification for lithological mapping [J]. Journal of African Earth Sciences, 2017, 129: 445-457.
[232] CRIPPA M, GUIZZARDI D, MUNTEAN M, et al. Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2 [J]. Earth System Science Data, 2018, 10(4): 1987-2013.
[233] 宫鹏, 张伟, 俞乐, 等. 全球地表覆盖制图研究新范式 [J]. 遥感学报, 2016, 20(05): 1002-1016.
[234] 吴一全, 曹照清, 陶飞翔. 结合多尺度几何分析和KICA的遥感图像变化检测 [J]. 遥感学报, 2015, 19(01): 126-133.
[235] 徐涵秋, 王美雅. 地表不透水面信息遥感的主要方法分析 [J]. 遥感学报, 2016, 20(05): 1270-1289.
[236] MASELLI F. Integration of high and low resolution NDVI data for monitoring vegetation in Mediterranean environments [J]. Remote Sensing of Environment, 1998: 23-45.
[237] MCFEETERS S K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features [J]. International Journal of Remote Sensing, 1996, 17(7): 1425-1432.
[238] 王恒, 杨昊翔, 张丽. 海上丝绸之路沿线区域植被覆盖变化特征 [J]. 遥感技术与应用, 2018, 33(04): 703-712.
[239] 尹思阳, 吴文瑾, 李新武. 基于遥感和气象数据的东南亚森林动态变化分析 [J]. 遥感技术与应用, 2019, (01): 166-175.
[240] LU D, WENG Q. Use of impervious surface in urban land-use classification [J]. Remote Sensing of Environment, 2006, 102(1-2): 146-160.
[241] ZHANG Q, SCHAAF C, SETO K C. The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity [J]. Remote Sensing of Environment, 2013, 129: 32-41.
[242] ZHANG Z, WEI M, PU D, et al. Assessment of annual composite images obtained by Google Earth Engine for urban areas mapping using random forest [J]. Remote Sensing, 2021, 13(4): 748.
[243] STEHMAN S V, ARORA M K, KASETKASEM T, et al. Estimation of fuzzy error matrix accuracy measures under stratified random sampling [J]. Photogrammetric Engineering & Remote Sensing, 2007, 73(2): 165-173.
[244] GELARO R, MCCARTY W, SUAREZ M J, et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) [J]. Journal of Climate, 2017, 30(14): 5419-5454.
[245] LI X, GONG P. An “exclusion-inclusion” framework for extracting human settlements in rapidly developing regions of China from Landsat images [J]. Remote Sensing of Environment, 2016, 186: 286-296.
[246] PALMER P I, JACOB D J, FIORE A M, et al. Mapping isoprene emissions over North America using formaldehyde column observations from space [J]. Journal of Geophysical Research: Atmospheres, 2003, 108(D6): 6598-6609.
[247] GUENTHER A B, JIANG X, HEALD C L, et al. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions [J]. Geoscientific Model Development, 2012, 5(6): 1471-1492.