Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/14305
Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and machine learning technique
Title: Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and machine learning technique
Authors: Tu, QiansiHase, FrankChen, ZihanSchneider, Matthias RESEARCHERID García Rodríguez, Omaira Elena ORCID RESEARCHERID Autor AEMETKhosrawi, FarahnazBlumenstock, ThomasLiu, FangQin, KaiLin, SongJiang, HongyanFang, Dianjun
Keywords: Nitrogen dioxide; Air pollution; Wind-assigned anomalies; Machine learning technique
Issue Date: 2022
Publisher: European Geosciences Union; Copernicus Publications
Citation: Atmospheric Measurement Techniques Discussions. Preprint 2022
Publisher version: https://doi.org/10.5194/amt-2022-176
Abstract: Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from three-year (April 2018 – March 2021) TROPOMI observations by combining a wind-assigned anomaly approach and a Machine Learning (ML) method, the so-called Gradient Descent. This combined approach is firstly applied to the Saudi Arabian capital city Riyadh, as a test site, and yields a total emission rate of 1.04×1026 molec./s. The ML-trained anomalies fit very well with the wind-assigned anomalies with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites where the cement plant and power plants are located and over areas along the highways. Using the same approach, an emission rate of 1.80×1025 molec./s is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable to the CAMS inventory.
Sponsorship : We also acknowledge the project of Joint R&D and Talents Program funded by the Qingdao Sino-German Institute of Intelligent Technologies (kh0100020213319) and the project of Transnational Interoperability Rules and Solution Patterns in Collaborative Production Networks based on IDS and GAIA-X funded by Ministry of Science and Technology, PRC (SQ2021YFE010470).
URI: http://hdl.handle.net/20.500.11765/14305
ISSN: 1867-1381
1867-8548
Appears in Collections:Artículos científicos 2019-2022


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