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| DOI | 10.1016/j.atmosres.2021.105628 |
| PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application | |
| Huang Y.; Zhang T.; Zhu Z.; Gong W.; Xia X. | |
| Date Issued | 2021 |
| ISSN | 0169-8095 |
| Volume | 258 |
| Other Abstract | Fine particle matter (PM2.5) significantly affects the atmospheric environment and human health. The satellite-derived aerosol optical depth (AOD), which could represent the concentration of atmospheric particles to a certain extent, is widely used for estimating ambient PM2.5 concentration, in combination with diverse auxiliary information. However, the general satellite-derived PM2.5 products exhibit limitation in the application and aggregate analysis of PM2.5 in urban areas, because of the moderate spatial resolution to match the urban landscape and low spatial coverage making it hard to describe airmass trajectory. In order to explore the potential application value of PM2.5 concentration products with relatively high spatial coverage and resolution, a two-stage machine learning and geo-statistics coupled model incorporating with a feedback mechanism was proposed in this study. To be specific, we firstly develop a hybrid back-propagation neural network coupled kriging with external drifting approach (BPNN-KED) for estimating 1-km daily PM2.5 concentration maps at high coverage over four urban agglomerations in China. The model performs well, with R2 up to 0.83 and root mean square error of 14.7 μg/m3 from cross-validation. The daily PM2.5 maps display an average spatial coverage exceeding 95%, and on an average, each grid produces 350 days of valid estimations annually. In addition, the extra value of the high-coverage PM2.5 estimates were explored through the more accurate aggregate analysis of urban PM2.5 pollution level. The advantage of the high-coverage PM2.5 estimation is demonstrated through daily PM2.5 hotspot identification over urban areas, providing substantially fine spatially resolved PM2.5 trends, which offers the potential for daily pollutant emission sources location through satellite remote sensing technology. Moreover, the spatiotemporally continuous PM2.5 concentrations possess the ability to capture polluted air mass trajectories, thereby offering observational support not only for evaluating the contribution from exogenous pollutants to local PM2.5 concentrations and but also for providing empirical references for haze warning. © 2021 Elsevier B.V. |
| enkeywords | 1-km resolution; Back-propagation neural network; High-coverage; Kriging with external drifting; PM2.5; Urban agglomerations |
| journal | Atmospheric Research
 |
| Document Type | 期刊论文 |
| Identifier | http://gcip.llas.ac.cn/handle/2XKMVOVA/236742 |
| Affiliation | State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China; College of Information Science and Engineering, Wuchang Shouyi University, Wuhan, 430064, China; Collaborative Innovation Centre for Geospatial Technology, Wuhan, 430079, China |
| Recommended Citation GB/T 7714 | Huang Y.,Zhang T.,Zhu Z.,et al. PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application[J],2021,258. |
| APA | Huang Y.,Zhang T.,Zhu Z.,Gong W.,&Xia X..(2021).PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application.Atmospheric Research,258. |
| MLA | Huang Y.,et al."PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application".Atmospheric Research 258(2021). |
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