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An optimization approach for hourly ozone simulation: a case study in Chongqing, China

Zhu, Songyan and Zeng, Qiaolin and Zhu, Hao and Xu, Jian and Gu, Jianbin and Wang, Yongqian and Chen, Liangfu (2021) An optimization approach for hourly ozone simulation: a case study in Chongqing, China. IEEE Geoscience and Remote Sensing Letters, 18 (11), pp. 1871-1875. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2020.3010416. ISSN 1545-598X.

[img] PDF - Only accessible within DLR bis December 2022 - Postprint version (accepted manuscript)

Official URL: https://ieeexplore.ieee.org/document/9153854


Continuous spatial knowledge is required to control the regional ozone pollution. Measurements from ground-level sites are beneficial to this goal, but their number is limited due to the huge expenses of site establishment, operation, and maintenance. Remote sensing seems a promising data source, but its application is challenged by bad weather conditions. Always covered by thick clouds, Chongqing, a populated industrial city in west China, is facing serious ozone pollution, but relevant studies here are relatively insufficient. Another alternative is estimating ozone by models. Well-performed models degrade in Chongqing partially due to the very complex terrain. Modeled hourly ozone does not agree with ground-level measurements. Therefore, an optimization approach is proposed to improve model estimates for such regions. This approach integrates the ground-level information (e.g., measured ozone and meteorology) through the employment of ResNet (Residual Network). ResNet overcomes the notorious vanishing gradient issue in classic neural networks, and the ability of learning complex systems is largely boosted. Ozone distribution is like a gray image that varies every second, which is not the case usually learned by ResNet. A color-image alike data structure is raised to address this ``nonstill image'' problem; according to the Taylor Expansion, polynomials can describe a complex system, and the errors are acceptable. To facilitate the usage in business operations, this approach is designed to be robust, inexpensive, and easy to use. The scheme of control site selection is discussed in detail. In cross-validations, this approach performs well, averaged R² is higher than 0.9 and the error is less than 5 μ g/m³.

Item URL in elib:https://elib.dlr.de/137986/
Document Type:Article
Title:An optimization approach for hourly ozone simulation: a case study in Chongqing, China
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zhu, SongyanDepartment of Geography, University of ExeterUNSPECIFIED
Zeng, QiaolinCollege of Computer Science and Technology, Chongqing University of Posts and TelecommunicationsUNSPECIFIED
Zhu, HaoChongqing Institute of Meteorological SciencesUNSPECIFIED
Xu, Jianjian.xu (at) dlr.dehttps://orcid.org/0000-0003-2348-125X
Gu, JianbinAerospace Information Research Institute, Chinese Academy of SciencesUNSPECIFIED
Wang, YongqianCollege of Environmental and Resource Science, Chengdu University of Information TechnologyUNSPECIFIED
Chen, LiangfuAerospace Information Research Institute, Chinese Academy of SciencesUNSPECIFIED
Date:November 2021
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2020.3010416
Page Range:pp. 1871-1875
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:China National Environmental Monitoring Centre (CNEMC), image recognition, nested air quality prediction modeling system (NAQ-PMS), ozone pollution, ResNet, thick clouds.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Xu, Dr.-Ing. Jian
Deposited On:25 Nov 2020 15:50
Last Modified:09 Dec 2021 18:30

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