Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate
文献类型：期刊论文
| 作者 | Chen, Sijie4; Tong, Bowen4; Russell, Lynn M.5; Wei, Jing6 ; Guo, Jianping7; Mao, Feiyue8; Liu, Dong4,9; Huang, Zhongwei10; Xie, Yun1; Qi, Bing11
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| 刊名 | REMOTE SENSING OF ENVIRONMENT
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| 出版日期 | 2022-11-01 |
| 卷号 | 281 |
| ISSN号 | 0034-4257 |
| 关键词 | MAIAC AOD Haze layer height Deep learning MPL PM2 5 |
| DOI | 10.1016/j.rse.2022.113224 |
| 通讯作者 | Liu, Dong(liudongopt@zju.edu.cn) |
| 英文摘要 | Satellite-derived aerosol optical depth (AOD) is popularly used to infer ground-level PM2.5 concentration due to its wide coverage. The fact that aerosols are largely confined in the atmospheric boundary layer makes boundary layer height (BLH) an important scale factor for AOD-based PM2.5 estimates. Our recent ground-based lidar observations, nevertheless, indicate that aerosol particles are heterogeneously mixed within the boundary layer, and even frequently reside above BLH, forming the residual layers (RL)-like pattern. To better sort out the underlying mechanism behind the above-mentioned phenomenon and the impact on hourly ground-level PM2.5 estimates from satellite-based AOD, we firstly propose a novel notion of haze layer height (HLH), which is calculated from Micro-Pulse Lidar (MPL) profile. Combined analysis of 3.5-year ground-based lidar profiles, CE318 AOD, and PM2.5 measurements show that the coefficient of determination (R2) between PM2.5 and AOD normalized with HLH increases from 0.49 to 0.61 for 90% of the dataset. Second, we applied HLH to an Autoencoder-based Deep Residual Network (ADRN) and tested the effect on satellite AOD-based PM2.5 estimation within a 300 km range surrounding the MPL station. With the aid of the AOD imputation technique, a similar improvement of using HLH instead is found on the regional scale PM2.5 estimation, which can be demonstrated by the comparison with air quality measurements and other machine learning models. The results show that using ADRN with HLH achieves the highest performance (mean R2 = 0.87, RMSE = 10.12 mu g/m3) among 4 machine learning models. This new approach, largely combining active and passive remote sensing data through artificial neural networks (CAPTA), shows improved accuracy and coverage of hourly PM2.5 estimation with aerosol vertical information and AOD calculation under clouds. In addition, further analysis showed that the average difference between morning and daily PM2.5 concentration could equate to an accuracy of 0.19-2.57 yrs. in terms of life expectancy, indicating that our new approach to the determination of PM2.5 from space sheds new insight into the assessment of the aerosol impact on public health. |
| WOS关键词 | UNITED-STATES ; AIR-POLLUTION ; SURFACE PM2.5 ; COLUMNAR AOD ; AEROSOL ; CHINA ; RETRIEVALS ; EXTINCTION ; PROFILES ; PRODUCTS |
| 资助项目 | National Key Research and Devel- opment Program of China[2016YFC1400900] ; Hangzhou Meteorological Bureau ; National Key Research and Development Program of China[2016YFC1400900] ; Scientific Research Projects of Zhejiang Administration for Market Regulation[2016YFC1400900] ; Zhejiang Provincial Natural Science Foundation of China[20200103] ; Agriculture and Social Development Research Project of Hangzhou[LR19D050001] ; Key Project of Science and Technology Plan of Zhejiang Meteorological Bureau[20201203B155] ; Zhejiang Provincial Basic Public Welfare Research Project[2021ZD13] ; [LGF22D050004] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:000858622400004 |
| 资助机构 | National Key Research and Devel- opment Program of China ; Hangzhou Meteorological Bureau ; National Key Research and Development Program of China ; Scientific Research Projects of Zhejiang Administration for Market Regulation ; Zhejiang Provincial Natural Science Foundation of China ; Agriculture and Social Development Research Project of Hangzhou ; Key Project of Science and Technology Plan of Zhejiang Meteorological Bureau ; Zhejiang Provincial Basic Public Welfare Research Project |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/128952]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Liu, Dong |
| 作者单位 | 1.Fuyang Meteorol Bur, Hangzhou 311499, Peoples R China 2.Zhejiang Univ, Intelligent Opt & Photon Res Ctr, Jiaxing Res Inst, Jiaxing 314000, Peoples R China 3.Jiaxing Key Lab Photon Sensing & Intelligent Imagi, Jiaxing 314000, Peoples R China 4.Zhejiang Univ, Coll Opt Sci & Engn, ZJU Hangzhou Global Sci & Technol Innovat Ctr, State Key Lab Modern Opt Instrumentat, Hangzhou 310027, Peoples R China 5.Univ Calif San Diego, Scripps Inst Oceanog, 9500 Gilman Dr, La Jolla, CA 92093 USA 6.Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, Dept Atmospher & Ocean Sci, College Pk, MD 20740 USA 7.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China 8.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China 9.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Atmospher Composit & Opt Radiat, Hefei 230031, Anhui, Peoples R China 10.Lanzhou Univ, Coll Atmospher Sci, Collaborat Innovat Ctr West Ecol Safety CIWES, Lanzhou 730000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Sijie,Tong, Bowen,Russell, Lynn M.,et al. Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate[J]. REMOTE SENSING OF ENVIRONMENT,2022,281. |
| APA | Chen, Sijie.,Tong, Bowen.,Russell, Lynn M..,Wei, Jing.,Guo, Jianping.,...&Wu, Lingyun.(2022).Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate.REMOTE SENSING OF ENVIRONMENT,281. |
| MLA | Chen, Sijie,et al."Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate".REMOTE SENSING OF ENVIRONMENT 281(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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