A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements
文献类型：期刊论文
| 作者 | Wang, Zijie2; Tian, Xin2; Xie, Pinhua1 ; Xu, Jin1; Zheng, Jiangyi1; Pan, Yifeng2; Zhang, Tianshu1; Fan, Guangqiang1
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| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
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| 出版日期 | 2024-11-15 |
| 卷号 | 951 |
| 关键词 | MAX-DOAS Convolutional Neural Networks Tropospheric ozone Vertical distribution |
| ISSN号 | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2024.175049 |
| 通讯作者 | Tian, Xin(xtian@ahu.edu.cn) ; Xie, Pinhua(phxie@aiofm.ac.cn) |
| 英文摘要 | The vertical distribution of tropospheric ozone (O3) is crucial for understanding atmospheric physicochemical processes. A Convolutional Neural Networks (CNN) method for the retrieval of tropospheric O3 vertical distribution from ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements to tackle the issue of stratospheric O3 absorption interference faced by MAX-DOAS in obtaining tropospheric O3 profiles. Firstly, a hybrid model, named PCA-F_Regression-SVR, is developed to screen features sensitive to O3 inversion based on the MAX-DOAS spectra and EAC4 reanalysis O3 profiles, which incorporates Principal Component Analysis (PCA), F_Regression function, and Support Vector Regression (SVR) algorithm. Thus, these screened features for ancillary inversion include the profiles of temperature, specific humidity, fraction of cloud coverage, eastward and northward wind, the profiles of SO2, NO2, and HCHO, as well as season and time features to serve as sensitive factors. Secondly, the preprocessed MAX-DOAS spectra dataset and the sensitive factor dataset are utilized as input, while the O3 profiles of the EAC4 reanalysis dataset incorporating the surface O3 concentrations are employed as output for constructing the CNN model. The Mean Absolute Percentage Error (MAPE) decreases from 26 % to approximately 19 %. Finally, the CNN model is applied for inversion and comparison of tropospheric O3 profiles using independent input data. The CNN model effectively reproduces the O3 profiles of the EAC4 dataset, showing a Gaussian-like spatial distribution with peaks primarily around 950 hPa (550 m). Since the reanalysis data used for model training has been smoothed, the CNN model is insensitive to extreme values. This behavior can be attributed to the MAPE loss function, which evaluates Absolute Percentage Errors (APEs) of O3 concentration at all altitudes, resulting in varying retrieval accuracy across different altitudes while maintaining overall MAPE control. Temporally, the CNN model tends to overestimate surface O3 in summer by around 20 mu g/m3, primarily due to the influence of the temperature feature in the sensitivity factor dataset. In conclusion, leveraging MAX-DOAS spectra enables the retrieval of tropospheric O3 vertical distri |
| WOS关键词 | MAX-DOAS MEASUREMENTS ; NORTH CHINA PLAIN ; ULTRAVIOLET-RADIATION ; CAMS REANALYSIS ; CROSS-SECTIONS ; SURFACE OZONE ; RURAL SITE ; PROFILES ; AEROSOL ; CHEMISTRY |
| 资助项目 | National Natural Science Foundation of China[42105132] ; National Natural Science Foundation of China[42030609] ; National Key Research and Development Program of China[2022YFC3700303] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001296837400001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/135869]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Tian, Xin; Xie, Pinhua |
| 作者单位 | 1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China 2.Anhui Univ, Inst Phys Sci & Informat Technol, Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei 230601, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Zijie,Tian, Xin,Xie, Pinhua,et al. A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2024,951. |
| APA | Wang, Zijie.,Tian, Xin.,Xie, Pinhua.,Xu, Jin.,Zheng, Jiangyi.,...&Fan, Guangqiang.(2024).A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements.SCIENCE OF THE TOTAL ENVIRONMENT,951. |
| MLA | Wang, Zijie,et al."A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements".SCIENCE OF THE TOTAL ENVIRONMENT 951(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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