Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Xingcheng, Lu"'
Publikováno v:
npj Climate and Atmospheric Science, Vol 6, Iss 1, Pp 1-11 (2023)
Abstract Trace metals in fine particulate matter (PM2.5) are of significant concern in environmental chemistry due to their toxicity and catalytic capability. An observation-constrained hybrid model is developed to resolve regional source contributio
Externí odkaz:
https://doaj.org/article/23e54958b5e4449f8f48cf2851942e3d
Autor:
XINGCHENG LU, JIMMY C. H. FUNG
Publikováno v:
Tellus: Series B, Chemical and Physical Meteorology, Vol 70, Iss 1, Pp 1-17 (2018)
This study analyses the sensitivity of PM2.5 simulation and source apportionment results by integrating different below-cloud washout (BCW) schemes from various models into the CAMx model during the rainy days (3–13 September 2010). Furthermore, th
Externí odkaz:
https://doaj.org/article/5327f01945a34890af222fb97d1ebcca
Publikováno v:
Atmospheric Chemistry & Physics Discussions; 2/19/2024, p1-19, 19p
Nitrogen oxides (NOx, mainly comprising NO and NO2) is the essential precursor of secondary air pollutants, such as ozone and particulate nitrate. To better understand NOx emission levels and acquire reasonable simulation results for further analysis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::411df57534b4021945464521cfe7ec91
https://doi.org/10.5194/egusphere-egu23-16003
https://doi.org/10.5194/egusphere-egu23-16003
Black carbon (BC) and brown carbon (BrC) have been considered light-absorbing components of particulate matter and affect weather and climate. Biomass burning (BB) emission from Southeast Asia (SEA) is a key source of BC and BrC on the planet. In thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af5690f8d370699274cf36025ca3f07e
https://doi.org/10.5194/egusphere-egu23-10734
https://doi.org/10.5194/egusphere-egu23-10734
Autor:
Yangyang Zhan, Yong Xu, Xingcheng Lu, Fei Zhou, Pengling Zheng, Dong Wang, Dongbo Cai, Shihui Yang, Shouwen Chen
Publikováno v:
ACS Sustainable Chemistry & Engineering. 9:17254-17265
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fad3698426d5b8dc9a38536790834eb
https://doi.org/10.1021/acssuschemeng.1c05511
https://doi.org/10.1021/acssuschemeng.1c05511
Autor:
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, Xingcheng Lu
Deep-learning frameworks can effectively forecast the air pollution data for individual stations by decoding time series data. However, most of the existing time-series-based deep-learning models use offline spatial interpolation strategies and thus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd7f5c109be768e63827e7c3f8995c37
https://gmd.copernicus.org/articles/15/8439/2022/
https://gmd.copernicus.org/articles/15/8439/2022/