气候变化领域集成服务门户(CCPortal): Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data

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DOI	10.1016/j.atmosenv.2020.117322
	Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data
	Qin K.; Han X.; Li D.; Xu J.; Li D.; Loyola D.; Zhou X.; Xue Y.; Zhang K.; Yuan L.
发表日期	2020
ISSN	1352-2310
卷号	224
英文摘要	The OMI NO2 standard product, OMNO2d, has been widely used in estimating surface NO2 concentrations. The Peking University Ozone Monitoring Instrument NO2 product (POMINO) is claimed to provide an improved quality over east-central China. This study estimated one year (Dec.2016–Nov.2017) of surface NO2 concentrations at satellite overpass time based on OMNO2d data and POMINO data, respectively. We used an extra-trees (ET) regression model to convey the non-linear relationship between surface NO2 and predictors, and compared the prediction accuracy with that of random forests (RF) regression model. The ET model showed a better estimation performance than the RF model, with the cross-validation R2 of 0.72 (RMSE = 9.20 μg/m3) and R2 of 0.70 (RMSE = 9.42 μg/m3) based on POMINO and OMNO2d data, respectively. The POMINO-derived monthly mean surface NO2 concentrations were closer to ground NO2 measurements than that OMNO2d-derived. Although the estimations from both satellite products were underestimated in polluted situations, the use of POMINO reduced the underestimation as compared to the use of OMNO2d data. © 2020 Elsevier Ltd
关键词	Extra trees NO2 OMI OMNO2d POMINO Random forest
语种	英语
scopus关键词	Decision trees; Forestry; Random forests; Regression analysis; Satellites; Ultraviolet spectrometers; Estimation performance; Extra-trees; Non-linear relationships; OMNO2d; Ozone monitoring instruments; POMINO; Prediction accuracy; Satellite products; Nitrogen oxides; nitrogen dioxide; ozone; concentration (composition); nitrous oxide; numerical model; regression analysis; satellite data; Article; concentration (parameter); controlled study; cross validation; prediction; priority journal; random forest; surface property; China
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/249273
作者单位	School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China; German Aerospace Center, Remote Sensing Technology Institute, Weßling, Germany
推荐引用方式 GB/T 7714	Qin K.,Han X.,Li D.,et al. Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data[J],2020,224.
APA	Qin K..,Han X..,Li D..,Xu J..,Li D..,...&Yuan L..(2020).Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data.ATMOSPHERIC ENVIRONMENT,224.
MLA	Qin K.,et al."Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data".ATMOSPHERIC ENVIRONMENT 224(2020).