Estimating 1-km PM2.5 concentrations based on a novel spatiotemporal parallel network STMSPNet in the Beijing-Tianjin-Hebei region
With the development of industry, the issue of air pollution is of great concern. Due to the sparsity of monitoring stations, acquiring full-coverage PM2.5 concentration remains challenging. The satellite remote sensing datasets provide a potential solution for estimation tasks by its spatiotemporal...
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| Vydané v: | Atmospheric environment (1994) Ročník 338; s. 120796 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.12.2024
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| ISSN: | 1352-2310 |
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| Abstract | With the development of industry, the issue of air pollution is of great concern. Due to the sparsity of monitoring stations, acquiring full-coverage PM2.5 concentration remains challenging. The satellite remote sensing datasets provide a potential solution for estimation tasks by its spatiotemporal virtue. In this study, the ellipsoidal coordinate system was introduced for the first time to improve the spatial coding method. A two-stage algorithm using satellite datasets and ground-site values was proposed to impute the missing AOD and estimate PM2.5 concentrations. Firstly, the Multilayer Perceptron (MLP) model was utilized for imputing the missing AOD, achieving superior accuracy (R2 = 0.929). Secondly, the training efficiency and the accuracy of traditional algorithm were enhanced by innovatively utilizing an efficient attention mechanism and a novel embedding layer. The concept of combining convolutional layers with different output channels and novel spatial preprocessing methods were also innovatively proposed. Consequently, the Spatiotemporal Multi-Sample Parallel Network (STMSPNet) was constructed to estimate daily PM2.5 concentrations. Finally, the best performance of this model was obtained by 10-fold cross-validation with R2 of 0.913 and RMSE of 10.637 μg/m3. In addition, this study analyses the changing patterns of PM2.5 concentrations in the Beijing-Tianjin-Hebei region from 2019 to 2023, taking into account the COVID-19 outbreak and extreme weather. The reasons for the changes are also discussed in depth through the air pollution control policies enacted by the Chinese government and regional factors. The results show that STMSPNet has a strong estimation advantage.
[Display omitted]
•We developed a two-stage model for AOD imputing and PM2.5 concentration estimation.•Using MLP model to obtain full-coverage AOD datasets.•Constructing spatiotemporal model with temporal and multi-sample module.•Application of Efficient Attention and new Embedding layer. |
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| AbstractList | With the development of industry, the issue of air pollution is of great concern. Due to the sparsity of monitoring stations, acquiring full-coverage PM2.5 concentration remains challenging. The satellite remote sensing datasets provide a potential solution for estimation tasks by its spatiotemporal virtue. In this study, the ellipsoidal coordinate system was introduced for the first time to improve the spatial coding method. A two-stage algorithm using satellite datasets and ground-site values was proposed to impute the missing AOD and estimate PM2.5 concentrations. Firstly, the Multilayer Perceptron (MLP) model was utilized for imputing the missing AOD, achieving superior accuracy (R2 = 0.929). Secondly, the training efficiency and the accuracy of traditional algorithm were enhanced by innovatively utilizing an efficient attention mechanism and a novel embedding layer. The concept of combining convolutional layers with different output channels and novel spatial preprocessing methods were also innovatively proposed. Consequently, the Spatiotemporal Multi-Sample Parallel Network (STMSPNet) was constructed to estimate daily PM2.5 concentrations. Finally, the best performance of this model was obtained by 10-fold cross-validation with R2 of 0.913 and RMSE of 10.637 μg/m3. In addition, this study analyses the changing patterns of PM2.5 concentrations in the Beijing-Tianjin-Hebei region from 2019 to 2023, taking into account the COVID-19 outbreak and extreme weather. The reasons for the changes are also discussed in depth through the air pollution control policies enacted by the Chinese government and regional factors. The results show that STMSPNet has a strong estimation advantage.
[Display omitted]
•We developed a two-stage model for AOD imputing and PM2.5 concentration estimation.•Using MLP model to obtain full-coverage AOD datasets.•Constructing spatiotemporal model with temporal and multi-sample module.•Application of Efficient Attention and new Embedding layer. |
| ArticleNumber | 120796 |
| Author | Zhu, Yuanyuan Zeng, Qiaolin Chen, Liangfu Li, Mingzheng Zhang, Ying Fan, Meng Tao, Jinhua Zhu, Hao |
| Author_xml | – sequence: 1 givenname: Qiaolin surname: Zeng fullname: Zeng, Qiaolin organization: School of Computer Science and Technology/School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China – sequence: 2 givenname: Mingzheng surname: Li fullname: Li, Mingzheng organization: School of Computer Science and Technology/School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China – sequence: 3 givenname: Meng surname: Fan fullname: Fan, Meng email: zengqiaolin2008@hotmail.com organization: The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China – sequence: 4 givenname: Jinhua surname: Tao fullname: Tao, Jinhua organization: The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China – sequence: 5 givenname: Liangfu surname: Chen fullname: Chen, Liangfu organization: The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China – sequence: 6 givenname: Ying orcidid: 0000-0001-6577-2376 surname: Zhang fullname: Zhang, Ying organization: The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China – sequence: 7 givenname: Hao surname: Zhu fullname: Zhu, Hao organization: Chongqing Institute of Meteorological Sciences, Chongqing, 401147, China – sequence: 8 givenname: Yuanyuan surname: Zhu fullname: Zhu, Yuanyuan organization: China National Environmental Monitoring Center, Beijing, 100012, China |
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| Cites_doi | 10.1109/TGRS.2023.3334492 10.1016/j.envpol.2017.12.070 10.1016/j.jclepro.2024.141259 10.1016/j.chemosphere.2020.128801 10.1016/j.scitotenv.2022.159673 10.1016/j.envsoft.2019.104600 10.1080/15481603.2022.2051382 10.1109/ACCESS.2020.2968744 10.1016/j.partic.2013.11.001 10.1016/j.jenvman.2023.118252 10.1016/j.jclepro.2020.123742 10.1016/j.scitotenv.2022.160446 10.3390/rs15164104 10.1080/15481603.2023.2262836 10.1016/j.scitotenv.2019.133983 10.1145/3586074 10.1016/j.atmosenv.2023.120193 10.1016/j.scitotenv.2019.04.299 10.1016/j.scitotenv.2018.02.255 10.1016/j.scitotenv.2023.169801 10.3390/rs14061515 10.1016/j.scitotenv.2020.141765 10.1016/j.atmosenv.2021.118212 10.1016/j.rse.2019.111221 10.3390/rs15174271 10.3390/rs14235967 10.3390/rs12020264 10.1016/j.envpol.2020.115042 10.1088/1748-9326/ad0dd9 10.1016/j.accre.2022.11.008 10.3390/rs14184432 10.1016/j.envpol.2022.120419 10.5194/essd-14-907-2022 10.1016/j.atmosenv.2023.119956 10.1016/j.atmosres.2020.105146 10.5194/acp-19-11031-2019 10.1016/j.rse.2021.112828 10.1109/ACCESS.2023.3266377 10.1016/j.jenvman.2023.118145 10.1016/j.atmosenv.2023.120216 10.1016/j.scs.2020.102329 10.1016/j.atmosenv.2022.119362 10.1139/er-2022-0125 10.1016/j.atmosenv.2013.07.012 10.1109/ACCESS.2024.3368034 10.1115/1.2128636 10.1016/j.atmosenv.2013.04.015 10.1016/j.scitotenv.2023.165061 10.3390/rs14205239 10.1016/j.jes.2020.06.031 10.1016/j.rse.2020.112136 10.1016/j.scitotenv.2024.170777 10.5194/acp-20-3273-2020 10.1016/j.buildenv.2023.110521 |
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| Keywords | Efficient attention Full-coverage AOD Spatiotemporal model PM2.5 |
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| References | Lin, Liang, Liu, Zhang, Xie, Yin, Ashraf (bib25) 2022; 14 Fournier, Caron, Aloise (bib13) 2023; 55 Chen, Huang, Xie, Liu, Hu (bib9) 2024; 912 Wang, Yao, Luo, Huang (bib41) 2022; 14 He, Ye, Wang, Luo, Song, Zhang (bib17) 2023; 342 Zeng, Wang, Zhu, Liu, Wang, Chen, Tao (bib55) 2024; 316 Chen, Yang, He, Yuan, Li, Zhu (bib5) 2023; 857 Gu, Chen, Wang, Gao, Wang, Liu (bib14) 2022; 13 Yu, Xi, Wu, Zheng (bib53) 2023; 15 Yu, Wong, Nazeer, Li, Kwok (bib52) 2024; 318 Yi, Mengfan, Kun, Yu, Xiaolu, Miao, Yan (bib50) 2019; 696 Pui, Chen, Zuo (bib33) 2014; 13 Wang, Wu, Wu (bib43) 2023; 9 Chen, Guo, Gu, Cheng, Yang, Zhan, Wei (bib8) 2023; 61 Chu, Zhang, Zhao, Li, Wu (bib11) 2022; 14 Sun, Fan, Zhang, Wang, Wang, Lyu, Zheng (bib39) 2022; 14 Chen, Gu, Guo, Cheng, Yang, Zhan, Fu (bib7) 2024; 914 Wei, Huang, Li, Xue, Peng, Sun, Cribb (bib44) 2019; 231 Patil, Boit, Gudivada, Nandigam (bib31) 2023; 11 Shen, Mingyuan, Haiyu, Shuai, Hongsheng (bib38) 2021 Sampson, Richards, Szpiro, Bergen, Sheppard, Larson, Kaufman (bib37) 2013; 75 Wang, Wu, Mao, Chen (bib42) 2024; 238 Qian, Ye, Jiang, Zhong, Zhang, Pinto, Huang, Li, Wei (bib35) 2024; 19 Byun, Schere (bib3) 2006; 59 Zhao, Zhang, Cheng, Fang, Wang, Zhou (bib62) 2023; 242 Zeng, Li, Tao, Fan, Chen, Wang, Wang (bib54) 2023; 309 Zhang, He, Zheng, Cui, Song, Fu (bib59) 2021; 268 Ding, Chen, Lu, Wang (bib12) 2021; 249 Li, Cheng (bib23) 2021; 101 Jiang, Chen, Nie, Ren, Xu, Tang (bib18) 2021; 248 Zeng, Wang, Tao, Fan, Zhu, Chen, Wang, Li (bib56) 2023; 896 Wei, Li, Cribb, Huang, Xue, Sun, Guo, Peng, Li, Lyapustin, Liu, Wu, Song (bib45) 2020; 20 Benas, Beloconi, Chrysoulakis (bib2) 2013; 79 Li (bib21) 2020; 12 Chu, Zhang, Liu, Ma, He (bib10) 2021; 99 Chen, Ye, Tong, Deng, Wang, Hong (bib6) 2022; 59 Ma, Zhang, Chen, Zhang, Liu (bib28) 2023; 15 Lei, Xu, Ma, Jin, Liu, Gong (bib20) 2022; 60 Liu, Zou, Li, Li, Li, Wu, Chen (bib26) 2023; 61 Yang, Meng, Wang (bib48) 2020; 8 Cai, Zhong, Liu, Du, Liu, Wu, Li, Yang, Wu, Gu, Jiang (bib4) 2023; 60 Miao, Tang, Ren, Kwan, Zhang (bib29) 2022; 290 Han, Zhao, Gao, Gu, Xin, Zhang (bib16) 2020; 61 Putri, Caraka, Toharudin, Kim, Chen, Gio, Sakti, Pontoh, Pratiwi, Nugraha, Azzahra, Cerelia, Darmawan, Faidah, Pardamean (bib34) 2024; 12 Zhu, Tang, Zhou, Li, Liu, Zhang, Zou, Li, Peng (bib63) 2023 Li, Zhao, Feng, Tian (bib22) 2024; 920 Xue, Zhang, Zhong, Li, Wei (bib47) 2021; 279 Wei, Li, Lyapustin, Sun, Peng, Xue, Su, Cribb (bib46) 2021; 252 Zhang, Chu, Wang, Zhang (bib61) 2018; 631–632 Liu, Cao, Zhao, Mulligan, Ye (bib27) 2018; 235 Nguyen, Le, Sung, Cheng, Wen, Wu, Aggarwal, Tsai (bib30) 2023; 343 Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin (bib40) 2017 Rodríguez-Urrego, Rodríguez-Urrego (bib36) 2020; 266 Yu, Masrur, Blaszczak-Boxe (bib51) 2023; 860 Bai, Li, Ma, Li, Li, Guo, Chang, Tan, Han (bib1) 2022; 14 Kumar, Kumar (bib19) 2024; 444 Guo, Wang, Pei, Su, Zhang, Wang (bib15) 2021; 751 Yang, Shi, Tang, Yang (bib49) 2022; 269 Zhai, Jacob, Wang, Shen, Li, Zhang, Gui, Zhao, Liao (bib57) 2019 Liang, Xia, Ke, Wang, Wen, Zhang, Zheng, Zimmermann (bib24) 2023; 37 Pu, Yoo (bib32) 2022; 315 Zhang, Zhang, Zhao, Lian (bib58) 2020; 124 Zhang, Zang, Wan, Wang, Zhang (bib60) 2019; 676 Jiang (10.1016/j.atmosenv.2024.120796_bib18) 2021; 248 Wang (10.1016/j.atmosenv.2024.120796_bib43) 2023; 9 Zhu (10.1016/j.atmosenv.2024.120796_bib63) 2023 Wei (10.1016/j.atmosenv.2024.120796_bib44) 2019; 231 Zhang (10.1016/j.atmosenv.2024.120796_bib58) 2020; 124 Ma (10.1016/j.atmosenv.2024.120796_bib28) 2023; 15 Yu (10.1016/j.atmosenv.2024.120796_bib51) 2023; 860 Chen (10.1016/j.atmosenv.2024.120796_bib8) 2023; 61 Ding (10.1016/j.atmosenv.2024.120796_bib12) 2021; 249 Zeng (10.1016/j.atmosenv.2024.120796_bib56) 2023; 896 Yang (10.1016/j.atmosenv.2024.120796_bib48) 2020; 8 Liu (10.1016/j.atmosenv.2024.120796_bib26) 2023; 61 Gu (10.1016/j.atmosenv.2024.120796_bib14) 2022; 13 Sun (10.1016/j.atmosenv.2024.120796_bib39) 2022; 14 Zhang (10.1016/j.atmosenv.2024.120796_bib61) 2018; 631–632 Fournier (10.1016/j.atmosenv.2024.120796_bib13) 2023; 55 Yu (10.1016/j.atmosenv.2024.120796_bib53) 2023; 15 Qian (10.1016/j.atmosenv.2024.120796_bib35) 2024; 19 Bai (10.1016/j.atmosenv.2024.120796_bib1) 2022; 14 Liang (10.1016/j.atmosenv.2024.120796_bib24) 2023; 37 Benas (10.1016/j.atmosenv.2024.120796_bib2) 2013; 79 Patil (10.1016/j.atmosenv.2024.120796_bib31) 2023; 11 Rodríguez-Urrego (10.1016/j.atmosenv.2024.120796_bib36) 2020; 266 Xue (10.1016/j.atmosenv.2024.120796_bib47) 2021; 279 Yang (10.1016/j.atmosenv.2024.120796_bib49) 2022; 269 Chen (10.1016/j.atmosenv.2024.120796_bib5) 2023; 857 Kumar (10.1016/j.atmosenv.2024.120796_bib19) 2024; 444 Shen (10.1016/j.atmosenv.2024.120796_bib38) 2021 Li (10.1016/j.atmosenv.2024.120796_bib23) 2021; 101 Chu (10.1016/j.atmosenv.2024.120796_bib10) 2021; 99 Wang (10.1016/j.atmosenv.2024.120796_bib42) 2024; 238 Wei (10.1016/j.atmosenv.2024.120796_bib45) 2020; 20 He (10.1016/j.atmosenv.2024.120796_bib17) 2023; 342 Pu (10.1016/j.atmosenv.2024.120796_bib32) 2022; 315 Lei (10.1016/j.atmosenv.2024.120796_bib20) 2022; 60 Lin (10.1016/j.atmosenv.2024.120796_bib25) 2022; 14 Yi (10.1016/j.atmosenv.2024.120796_bib50) 2019; 696 Liu (10.1016/j.atmosenv.2024.120796_bib27) 2018; 235 Guo (10.1016/j.atmosenv.2024.120796_bib15) 2021; 751 Sampson (10.1016/j.atmosenv.2024.120796_bib37) 2013; 75 Yu (10.1016/j.atmosenv.2024.120796_bib52) 2024; 318 Zhai (10.1016/j.atmosenv.2024.120796_bib57) 2019 Han (10.1016/j.atmosenv.2024.120796_bib16) 2020; 61 Pui (10.1016/j.atmosenv.2024.120796_bib33) 2014; 13 Zhao (10.1016/j.atmosenv.2024.120796_bib62) 2023; 242 Cai (10.1016/j.atmosenv.2024.120796_bib4) 2023; 60 Zhang (10.1016/j.atmosenv.2024.120796_bib60) 2019; 676 Chen (10.1016/j.atmosenv.2024.120796_bib6) 2022; 59 Chen (10.1016/j.atmosenv.2024.120796_bib9) 2024; 912 Li (10.1016/j.atmosenv.2024.120796_bib22) 2024; 920 Wei (10.1016/j.atmosenv.2024.120796_bib46) 2021; 252 Nguyen (10.1016/j.atmosenv.2024.120796_bib30) 2023; 343 Li (10.1016/j.atmosenv.2024.120796_bib21) 2020; 12 Wang (10.1016/j.atmosenv.2024.120796_bib41) 2022; 14 Chu (10.1016/j.atmosenv.2024.120796_bib11) 2022; 14 Byun (10.1016/j.atmosenv.2024.120796_bib3) 2006; 59 Miao (10.1016/j.atmosenv.2024.120796_bib29) 2022; 290 Vaswani (10.1016/j.atmosenv.2024.120796_bib40) Zhang (10.1016/j.atmosenv.2024.120796_bib59) 2021; 268 Putri (10.1016/j.atmosenv.2024.120796_bib34) 2024; 12 Zeng (10.1016/j.atmosenv.2024.120796_bib54) 2023; 309 Zeng (10.1016/j.atmosenv.2024.120796_bib55) 2024; 316 Chen (10.1016/j.atmosenv.2024.120796_bib7) 2024; 914 |
| References_xml | – volume: 79 start-page: 448 year: 2013 end-page: 454 ident: bib2 article-title: Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations publication-title: Atmos. Environ. – volume: 914 year: 2024 ident: bib7 article-title: Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model publication-title: Sci. Total Environ. – volume: 235 start-page: 272 year: 2018 end-page: 282 ident: bib27 article-title: Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach publication-title: Environ. Pollut. – volume: 342 year: 2023 ident: bib17 article-title: Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020 publication-title: J. Environ. Manag. – volume: 920 year: 2024 ident: bib22 article-title: Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: a case study in Taiyuan publication-title: China. Sci. Total Environ. – volume: 238 year: 2024 ident: bib42 article-title: A forecasting framework on fusion of spatiotemporal features for multi-station PM2.5 publication-title: Expert Syst. Appl. – volume: 231 year: 2019 ident: bib44 article-title: Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach publication-title: Remote Sens. Environ. – volume: 696 year: 2019 ident: bib50 article-title: Research on PM2.5 estimation and prediction method and changing characteristics analysis under long temporal and large spatial scale - a case study in China typical regions publication-title: Sci. Total Environ. – volume: 316 year: 2024 ident: bib55 article-title: Estimating daily concentrations of near-surface CO, NO2, and O3 simultaneously over China based on spatiotemporal multi-task transformer model publication-title: Atmos. Environ. – volume: 11 start-page: 36120 year: 2023 end-page: 36146 ident: bib31 article-title: A survey of text representation and embedding techniques in NLP publication-title: IEEE Access – volume: 676 start-page: 535 year: 2019 end-page: 544 ident: bib60 article-title: Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8 publication-title: Sci. Total Environ. – volume: 55 start-page: 1 year: 2023 end-page: 40 ident: bib13 article-title: A practical survey on faster and lighter transformers publication-title: ACM Comput. Surv. – volume: 59 start-page: 51 year: 2006 end-page: 77 ident: bib3 article-title: Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system publication-title: Appl. Mech. Rev. – volume: 9 year: 2023 ident: bib43 article-title: A spatiotemporal XGBoost model for PM2.5 concentration prediction and its application in Shanghai publication-title: Heliyon – volume: 309 year: 2023 ident: bib54 article-title: Full-coverage estimation of PM2.5 in the Beijing-Tianjin-Hebei region by using a two-stage model publication-title: Atmos. Environ. – volume: 37 start-page: 14329 year: 2023 end-page: 14337 ident: bib24 article-title: AirFormer: predicting nationwide air quality in China with transformers publication-title: Proc. AAAI Conf. Artif. Intell. – volume: 14 start-page: 5239 year: 2022 ident: bib25 article-title: Estimating PM2.5 concentrations using the machine learning RF-XGBoost model in guanzhong urban agglomeration, China publication-title: Remote Sens. – volume: 249 year: 2021 ident: bib12 article-title: A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei publication-title: Atmos. Environ. – volume: 896 year: 2023 ident: bib56 article-title: Estimation of ground-level O3 concentration in the Yangtze River Delta region based on a high-performance spatiotemporal model MixNet publication-title: Sci. Total Environ. – volume: 59 start-page: 670 year: 2022 end-page: 685 ident: bib6 article-title: A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020 publication-title: GIScience Remote Sens. – volume: 60 start-page: 1 year: 2022 end-page: 14 ident: bib20 article-title: Full coverage estimation of the PM concentration across China based on an adaptive spatiotemporal approach publication-title: IEEE Trans. Geosci. Rem. Sens. – year: 2017 ident: bib40 article-title: Attention is all you need – volume: 75 start-page: 383 year: 2013 end-page: 392 ident: bib37 article-title: A regionalized national universal kriging model using Partial Least Squares regression for estimating annual PM2.5 concentrations in epidemiology publication-title: Atmos. Environ. – volume: 269 year: 2022 ident: bib49 article-title: Geographical and temporal encoding for improving the estimation of PM2.5 concentrations in China using end-to-end gradient boosting publication-title: Remote Sens. Environ. – volume: 857 year: 2023 ident: bib5 article-title: High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method publication-title: Sci. Total Environ. – volume: 61 start-page: 1 year: 2023 end-page: 17 ident: bib26 article-title: An efficient and accurate model coupled with spatiotemporal kalman filter and linear mixed effect for hourly PM publication-title: IEEE Trans. Geosci. Rem. Sens. – volume: 14 start-page: 907 year: 2022 end-page: 927 ident: bib1 article-title: LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion publication-title: Earth Syst. Sci. Data – volume: 12 start-page: 28988 year: 2024 end-page: 29003 ident: bib34 article-title: Fine-tuning of predictive models CNN-LSTM and CONV-LSTM for nowcasting PM publication-title: IEEE Access – volume: 99 start-page: 346 year: 2021 end-page: 353 ident: bib10 article-title: Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic publication-title: J. Environ. Sci. – volume: 20 start-page: 3273 year: 2020 end-page: 3289 ident: bib45 article-title: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees publication-title: Atmos. Chem. Phys. – volume: 101 year: 2021 ident: bib23 article-title: Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach publication-title: Int. J. Appl. Earth Obs. Geoinformation – volume: 912 year: 2024 ident: bib9 article-title: Improved prediction of hourly PM2.5 concentrations with a long short-term memory and spatio-temporal causal convolutional network deep learning model publication-title: Sci. Total Environ. – volume: 279 year: 2021 ident: bib47 article-title: Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region publication-title: J. Clean. Prod. – year: 2019 ident: bib57 article-title: Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology publication-title: Atmos. Chem. Phys. – volume: 124 year: 2020 ident: bib58 article-title: Constructing a PM2.5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks publication-title: Environ. Model. Software – volume: 444 year: 2024 ident: bib19 article-title: Multi-view Stacked CNN-BiLSTM (MvS CNN-BiLSTM) for urban P M 2 . 5 concentration prediction of India's polluted cities publication-title: J. Clean. Prod. – volume: 19 year: 2024 ident: bib35 article-title: Rapid attribution of the record-breaking heatwave event in North China in June 2023 and future risks publication-title: Environ. Res. Lett. – volume: 252 year: 2021 ident: bib46 article-title: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications publication-title: Remote Sens. Environ. – volume: 15 start-page: 4104 year: 2023 ident: bib53 article-title: Spatiotemporal weighted for improving the satellite-based high-resolution ground PM2.5 estimation using the Light gradient boosting machine publication-title: Rem. Sens. – volume: 15 start-page: 4271 year: 2023 ident: bib28 article-title: High spatial resolution nighttime PM2.5 datasets in the Beijing–Tianjin–Hebei region from 2015 to 2021 using VIIRS/DNB and deep learning model publication-title: Rem. Sens. – volume: 266 year: 2020 ident: bib36 article-title: Air quality during the COVID-19: PM2.5 analysis in the 50 most polluted capital cities in the world publication-title: Environ. Pollut. – volume: 242 year: 2023 ident: bib62 article-title: Investigate the effects of urban land use on PM2.5 concentration: an application of deep learning simulation publication-title: Build. Environ. – volume: 248 year: 2021 ident: bib18 article-title: Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model publication-title: Atmos. Res. – volume: 14 start-page: 4432 year: 2022 ident: bib11 article-title: Spatiotemporally continuous reconstruction of retrieved PM2.5 data using an autogeoi-stacking model in the beijing-tianjin-hebei region, China publication-title: Rem. Sens. – volume: 318 year: 2024 ident: bib52 article-title: A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique publication-title: Atmos. Environ. – volume: 860 year: 2023 ident: bib51 article-title: Predicting hourly PM2.5 concentrations in wildfire-prone areas using a SpatioTemporal Transformer model publication-title: Sci. Total Environ. – volume: 61 start-page: 1 year: 2023 end-page: 15 ident: bib8 article-title: A spatial neighborhood deep neural network model for PM publication-title: IEEE Trans. Geosci. Rem. Sens. – volume: 14 start-page: 5967 year: 2022 ident: bib39 article-title: Tempo-spatial distributions and transport characteristics of two dust events over northern China in March 2021 publication-title: Rem. Sens. – volume: 631–632 start-page: 904 year: 2018 end-page: 911 ident: bib61 article-title: Predicting daily PM2.5 concentrations in Texas using high-resolution satellite aerosol optical depth publication-title: Sci. Total Environ. – volume: 290 year: 2022 ident: bib29 article-title: Estimation of daily ground-level PM2.5 concentrations over the Pearl River Delta using 1 km resolution MODIS AOD based on multi-feature BiLSTM publication-title: Atmos. Environ. – volume: 13 start-page: 835 year: 2022 end-page: 842 ident: bib14 article-title: Extreme precipitation over northern China in autumn 2021 and joint contributions of tropical and mid-latitude factors publication-title: Adv. Clim. Change Res. – volume: 61 year: 2020 ident: bib16 article-title: Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models publication-title: Sustain. Cities Soc. – volume: 14 start-page: 1515 year: 2022 ident: bib41 article-title: Estimating high-resolution PM2.5 concentrations by fusing satellite AOD and smartphone photographs using a convolutional neural network and ensemble learning publication-title: Rem. Sens. – volume: 13 start-page: 1 year: 2014 end-page: 26 ident: bib33 article-title: PM 2.5 in China: measurements, sources, visibility and health effects, and mitigation publication-title: Particuology – volume: 12 start-page: 264 year: 2020 ident: bib21 article-title: A robust deep learning approach for spatiotemporal estimation of satellite AOD and PM2.5 publication-title: Rem. Sens. – start-page: 3530 year: 2021 end-page: 3538 ident: bib38 article-title: Efficient attention: attention with linear complexities publication-title: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). Presented at the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) – volume: 343 year: 2023 ident: bib30 article-title: The influence of COVID-19 pandemic on PM2.5 air quality in Northern Taiwan from Q1 2020 to Q2 2021 publication-title: J. Environ. Manag. – volume: 60 year: 2023 ident: bib4 article-title: An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud publication-title: GIScience Remote Sens. – year: 2023 ident: bib63 article-title: Research progress, challenges, and prospects of PM publication-title: Environ. Rev. – volume: 8 start-page: 18305 year: 2020 end-page: 18315 ident: bib48 article-title: Data-driven feature extraction for analog circuit fault diagnosis using 1-D convolutional neural network publication-title: IEEE Access – volume: 268 year: 2021 ident: bib59 article-title: Satellite-based ground PM2.5 estimation using a gradient boosting decision tree publication-title: Chemosphere – volume: 751 year: 2021 ident: bib15 article-title: Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018 publication-title: Sci. Total Environ. – volume: 315 year: 2022 ident: bib32 article-title: A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data publication-title: Environ. Pollut. – volume: 61 start-page: 1 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib26 article-title: An efficient and accurate model coupled with spatiotemporal kalman filter and linear mixed effect for hourly PM 2.5 mapping publication-title: IEEE Trans. Geosci. Rem. Sens. doi: 10.1109/TGRS.2023.3334492 – volume: 235 start-page: 272 year: 2018 ident: 10.1016/j.atmosenv.2024.120796_bib27 article-title: Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2017.12.070 – ident: 10.1016/j.atmosenv.2024.120796_bib40 – volume: 444 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib19 article-title: Multi-view Stacked CNN-BiLSTM (MvS CNN-BiLSTM) for urban P M 2 . 5 concentration prediction of India's polluted cities publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2024.141259 – volume: 268 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib59 article-title: Satellite-based ground PM2.5 estimation using a gradient boosting decision tree publication-title: Chemosphere doi: 10.1016/j.chemosphere.2020.128801 – volume: 857 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib5 article-title: High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2022.159673 – volume: 124 year: 2020 ident: 10.1016/j.atmosenv.2024.120796_bib58 article-title: Constructing a PM2.5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks publication-title: Environ. Model. Software doi: 10.1016/j.envsoft.2019.104600 – volume: 59 start-page: 670 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib6 article-title: A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020 publication-title: GIScience Remote Sens. doi: 10.1080/15481603.2022.2051382 – volume: 912 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib9 article-title: Improved prediction of hourly PM2.5 concentrations with a long short-term memory and spatio-temporal causal convolutional network deep learning model publication-title: Sci. Total Environ. – volume: 8 start-page: 18305 year: 2020 ident: 10.1016/j.atmosenv.2024.120796_bib48 article-title: Data-driven feature extraction for analog circuit fault diagnosis using 1-D convolutional neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2968744 – volume: 13 start-page: 1 year: 2014 ident: 10.1016/j.atmosenv.2024.120796_bib33 article-title: PM 2.5 in China: measurements, sources, visibility and health effects, and mitigation publication-title: Particuology doi: 10.1016/j.partic.2013.11.001 – volume: 343 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib30 article-title: The influence of COVID-19 pandemic on PM2.5 air quality in Northern Taiwan from Q1 2020 to Q2 2021 publication-title: J. Environ. Manag. doi: 10.1016/j.jenvman.2023.118252 – volume: 279 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib47 article-title: Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2020.123742 – volume: 860 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib51 article-title: Predicting hourly PM2.5 concentrations in wildfire-prone areas using a SpatioTemporal Transformer model publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2022.160446 – volume: 15 start-page: 4104 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib53 article-title: Spatiotemporal weighted for improving the satellite-based high-resolution ground PM2.5 estimation using the Light gradient boosting machine publication-title: Rem. Sens. doi: 10.3390/rs15164104 – volume: 60 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib4 article-title: An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud publication-title: GIScience Remote Sens. doi: 10.1080/15481603.2023.2262836 – start-page: 3530 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib38 article-title: Efficient attention: attention with linear complexities – volume: 696 year: 2019 ident: 10.1016/j.atmosenv.2024.120796_bib50 article-title: Research on PM2.5 estimation and prediction method and changing characteristics analysis under long temporal and large spatial scale - a case study in China typical regions publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2019.133983 – volume: 55 start-page: 1 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib13 article-title: A practical survey on faster and lighter transformers publication-title: ACM Comput. Surv. doi: 10.1145/3586074 – volume: 316 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib55 article-title: Estimating daily concentrations of near-surface CO, NO2, and O3 simultaneously over China based on spatiotemporal multi-task transformer model publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2023.120193 – volume: 37 start-page: 14329 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib24 article-title: AirFormer: predicting nationwide air quality in China with transformers publication-title: Proc. AAAI Conf. Artif. Intell. – volume: 676 start-page: 535 year: 2019 ident: 10.1016/j.atmosenv.2024.120796_bib60 article-title: Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8 publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2019.04.299 – volume: 631–632 start-page: 904 year: 2018 ident: 10.1016/j.atmosenv.2024.120796_bib61 article-title: Predicting daily PM2.5 concentrations in Texas using high-resolution satellite aerosol optical depth publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2018.02.255 – volume: 914 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib7 article-title: Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2023.169801 – volume: 9 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib43 article-title: A spatiotemporal XGBoost model for PM2.5 concentration prediction and its application in Shanghai publication-title: Heliyon – volume: 14 start-page: 1515 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib41 article-title: Estimating high-resolution PM2.5 concentrations by fusing satellite AOD and smartphone photographs using a convolutional neural network and ensemble learning publication-title: Rem. Sens. doi: 10.3390/rs14061515 – volume: 751 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib15 article-title: Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018 publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2020.141765 – volume: 249 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib12 article-title: A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2021.118212 – volume: 231 year: 2019 ident: 10.1016/j.atmosenv.2024.120796_bib44 article-title: Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.111221 – volume: 15 start-page: 4271 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib28 article-title: High spatial resolution nighttime PM2.5 datasets in the Beijing–Tianjin–Hebei region from 2015 to 2021 using VIIRS/DNB and deep learning model publication-title: Rem. Sens. doi: 10.3390/rs15174271 – volume: 14 start-page: 5967 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib39 article-title: Tempo-spatial distributions and transport characteristics of two dust events over northern China in March 2021 publication-title: Rem. Sens. doi: 10.3390/rs14235967 – volume: 238 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib42 article-title: A forecasting framework on fusion of spatiotemporal features for multi-station PM2.5 publication-title: Expert Syst. Appl. – volume: 12 start-page: 264 year: 2020 ident: 10.1016/j.atmosenv.2024.120796_bib21 article-title: A robust deep learning approach for spatiotemporal estimation of satellite AOD and PM2.5 publication-title: Rem. Sens. doi: 10.3390/rs12020264 – volume: 61 start-page: 1 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib8 article-title: A spatial neighborhood deep neural network model for PM 2.5 estimation across China publication-title: IEEE Trans. Geosci. Rem. Sens. – volume: 60 start-page: 1 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib20 article-title: Full coverage estimation of the PM concentration across China based on an adaptive spatiotemporal approach publication-title: IEEE Trans. Geosci. Rem. Sens. – volume: 266 year: 2020 ident: 10.1016/j.atmosenv.2024.120796_bib36 article-title: Air quality during the COVID-19: PM2.5 analysis in the 50 most polluted capital cities in the world publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2020.115042 – volume: 19 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib35 article-title: Rapid attribution of the record-breaking heatwave event in North China in June 2023 and future risks publication-title: Environ. Res. Lett. doi: 10.1088/1748-9326/ad0dd9 – volume: 13 start-page: 835 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib14 article-title: Extreme precipitation over northern China in autumn 2021 and joint contributions of tropical and mid-latitude factors publication-title: Adv. Clim. Change Res. doi: 10.1016/j.accre.2022.11.008 – volume: 14 start-page: 4432 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib11 article-title: Spatiotemporally continuous reconstruction of retrieved PM2.5 data using an autogeoi-stacking model in the beijing-tianjin-hebei region, China publication-title: Rem. Sens. doi: 10.3390/rs14184432 – volume: 315 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib32 article-title: A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2022.120419 – volume: 14 start-page: 907 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib1 article-title: LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion publication-title: Earth Syst. Sci. Data doi: 10.5194/essd-14-907-2022 – volume: 309 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib54 article-title: Full-coverage estimation of PM2.5 in the Beijing-Tianjin-Hebei region by using a two-stage model publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2023.119956 – volume: 248 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib18 article-title: Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model publication-title: Atmos. Res. doi: 10.1016/j.atmosres.2020.105146 – year: 2019 ident: 10.1016/j.atmosenv.2024.120796_bib57 article-title: Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology publication-title: Atmos. Chem. Phys. doi: 10.5194/acp-19-11031-2019 – volume: 269 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib49 article-title: Geographical and temporal encoding for improving the estimation of PM2.5 concentrations in China using end-to-end gradient boosting publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112828 – volume: 11 start-page: 36120 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib31 article-title: A survey of text representation and embedding techniques in NLP publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3266377 – volume: 342 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib17 article-title: Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020 publication-title: J. Environ. Manag. doi: 10.1016/j.jenvman.2023.118145 – volume: 318 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib52 article-title: A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2023.120216 – volume: 61 year: 2020 ident: 10.1016/j.atmosenv.2024.120796_bib16 article-title: Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models publication-title: Sustain. Cities Soc. doi: 10.1016/j.scs.2020.102329 – volume: 290 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib29 article-title: Estimation of daily ground-level PM2.5 concentrations over the Pearl River Delta using 1 km resolution MODIS AOD based on multi-feature BiLSTM publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2022.119362 – year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib63 article-title: Research progress, challenges, and prospects of PM 2.5 concentration estimation using satellite data publication-title: Environ. Rev. doi: 10.1139/er-2022-0125 – volume: 79 start-page: 448 year: 2013 ident: 10.1016/j.atmosenv.2024.120796_bib2 article-title: Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2013.07.012 – volume: 12 start-page: 28988 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib34 article-title: Fine-tuning of predictive models CNN-LSTM and CONV-LSTM for nowcasting PM 2.5 level publication-title: IEEE Access doi: 10.1109/ACCESS.2024.3368034 – volume: 59 start-page: 51 year: 2006 ident: 10.1016/j.atmosenv.2024.120796_bib3 article-title: Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system publication-title: Appl. Mech. Rev. doi: 10.1115/1.2128636 – volume: 75 start-page: 383 year: 2013 ident: 10.1016/j.atmosenv.2024.120796_bib37 article-title: A regionalized national universal kriging model using Partial Least Squares regression for estimating annual PM2.5 concentrations in epidemiology publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2013.04.015 – volume: 101 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib23 article-title: Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach publication-title: Int. J. Appl. Earth Obs. Geoinformation – volume: 896 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib56 article-title: Estimation of ground-level O3 concentration in the Yangtze River Delta region based on a high-performance spatiotemporal model MixNet publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2023.165061 – volume: 14 start-page: 5239 year: 2022 ident: 10.1016/j.atmosenv.2024.120796_bib25 article-title: Estimating PM2.5 concentrations using the machine learning RF-XGBoost model in guanzhong urban agglomeration, China publication-title: Remote Sens. doi: 10.3390/rs14205239 – volume: 99 start-page: 346 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib10 article-title: Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic publication-title: J. Environ. Sci. doi: 10.1016/j.jes.2020.06.031 – volume: 252 year: 2021 ident: 10.1016/j.atmosenv.2024.120796_bib46 article-title: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112136 – volume: 920 year: 2024 ident: 10.1016/j.atmosenv.2024.120796_bib22 article-title: Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: a case study in Taiyuan publication-title: China. Sci. Total Environ. doi: 10.1016/j.scitotenv.2024.170777 – volume: 20 start-page: 3273 year: 2020 ident: 10.1016/j.atmosenv.2024.120796_bib45 article-title: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees publication-title: Atmos. Chem. Phys. doi: 10.5194/acp-20-3273-2020 – volume: 242 year: 2023 ident: 10.1016/j.atmosenv.2024.120796_bib62 article-title: Investigate the effects of urban land use on PM2.5 concentration: an application of deep learning simulation publication-title: Build. Environ. doi: 10.1016/j.buildenv.2023.110521 |
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| Snippet | With the development of industry, the issue of air pollution is of great concern. Due to the sparsity of monitoring stations, acquiring full-coverage PM2.5... |
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| StartPage | 120796 |
| SubjectTerms | AOD Efficient attention Full-coverage PM2.5 Spatiotemporal model |
| Title | Estimating 1-km PM2.5 concentrations based on a novel spatiotemporal parallel network STMSPNet in the Beijing-Tianjin-Hebei region |
| URI | https://dx.doi.org/10.1016/j.atmosenv.2024.120796 |
| Volume | 338 |
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