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Abbreviation : CTMs
Long Form : chemical transport models
No. Year Title Co-occurring Abbreviation
2020 Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions. ---
2020 Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism. ---
2019 Atmosphere-terrestrial exchange of gaseous elemental mercury: parameterization improvement through direct comparison with measured ecosystem fluxes. ---
2019 Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014-2017. CMAQ
2019 Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000-2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations. AOD, CI, HD-expansion, ML
2019 To what extent can the below-cloud washout effect influence the PM2.5? A combined observational and modeling study. BCW, CAMx
2018 Attributing differences in the fate of lateral boundary ozone in AQMEII3 models to physical process representations. ---
2018 Errors and improvements in the use of archived meteorological data for chemical transport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology. GCMs, GMAO
2017 Below-cloud wet scavenging of soluble inorganic ions by rain in Beijing during the summer of 2014. WSC
10  2015 Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning. AOD, CV, GASP, GBM
11  2013 Introductory lecture: atmospheric organic aerosols: insights from the combination of measurements and chemical transport models. AMS, CCN, OA
12  2009 Simulating the formation of semivolatile primary and secondary organic aerosol in a regional chemical transport model. IMPROVE, PAQS, POA, SOA, STN
13  2005 Reconciliation and interpretation of Big Bend National Park particulate sulfur source apportionment: results from the Big Bend Regional Aerosol and Visibility Observational Study--part I. BRAVO