Articles | Volume 13, issue 8
https://doi.org/10.5194/gmd-13-3489-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-3489-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
05 Aug 2020
Model evaluation paper |
| 05 Aug 2020
| 05 Aug 2020
Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2)
Arman Pouyaei
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, TX, USA
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, TX, USA
Jia Jung
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, TX, USA
Bavand Sadeghi
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, TX, USA
Chul Han Song
School of Earth Science and Environmental Engineering, Gwangju Institute
of Science and Technology (GIST), Gwangju, South Korea
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27 citations as recorded by crossref.
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- The Impact of Springtime‐Transported Air Pollutants on Local Air Quality With Satellite‐Constrained NOx Emission Adjustments Over East Asia J. Jung et al. https://doi.org/10.1029/2021JD035251
- Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980–2021) Y. Ouma et al. https://doi.org/10.1016/j.scitotenv.2023.169027
- A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach M. Ghahremanloo et al. https://doi.org/10.1016/j.atmosenv.2022.118944
- A PM2.5 Concentration Prediction Model Based on CART–BLS L. Wang et al. https://doi.org/10.3390/atmos13101674
- Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks A. Sayeed et al. https://doi.org/10.1016/j.atmosenv.2021.118376
- Wildfires impact on PM2.5 concentration in galicia Spain C. Quishpe-Vásquez et al. https://doi.org/10.1016/j.jenvman.2024.122093
- Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network Y. Lops et al. https://doi.org/10.1007/s00521-023-08558-1
- Seasonal Characteristics of Long-Range Transport and Potential Associated Sources of Particulate Matter (Pm10) Pollution at the Station Elk, Poland, on 2021-2022 Data S. Abdo & Y. Koroleva https://doi.org/10.24057/2071-9388-2022-2461
- Influence of seasonal variability on source characteristics of VOCs at Houston industrial area B. Sadeghi et al. https://doi.org/10.1016/j.atmosenv.2022.119077
- An analysis of the temporal variability in volatile organic compounds (VOCs) within megacity Seoul and an identification of their sources S. Kang et al. https://doi.org/10.1016/j.apr.2022.101338
- Spatiotemporal characteristics of airport CO, NO2 and SO2 concentrations from aircraft emissions in China (2006–2023) Q. Cui & X. Sun https://doi.org/10.1093/tse/tdag010
- Advances in air quality modeling through artificial intelligence, machine learning, and deep learning: A comprehensive review D. Nelson et al. https://doi.org/10.1016/j.scitotenv.2026.181593
- Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy J. Park et al. https://doi.org/10.5194/amt-16-3039-2023
- Air pollution analysis in Northwestern South America: A new Lagrangian framework A. Casallas et al. https://doi.org/10.1016/j.scitotenv.2023.167350
- Co-attention trajectory prediction by mining heterogeneous interactive relationships L. Zhang et al. https://doi.org/10.1007/s11042-022-13942-5
- Impact of the COVID-19 outbreak on air pollution levels in East Asia M. Ghahremanloo et al. https://doi.org/10.1016/j.scitotenv.2020.142226
- Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms I. Yeo et al. https://doi.org/10.1007/s00521-021-06082-8
- Deep learning solver for solving advection–diffusion equation in comparison to finite difference methods A. Salman et al. https://doi.org/10.1016/j.cnsns.2022.106780
- Machine learning-driven regional prediction of PM2.5 concentrations in the eastern mediterranean bridging spatial data gaps in air quality monitoring İ. Gürtepe et al. https://doi.org/10.1016/j.envsoft.2025.106586
- A review of microplastic contamination in the cryosphere I. Qayoom et al. https://doi.org/10.1016/j.isci.2025.114414
- The sensitivities of ozone and PM2.5 concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system J. Park et al. https://doi.org/10.1016/j.envpol.2022.119419
- A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions S. Mousavinezhad et al. https://doi.org/10.1016/j.atmosres.2021.105599
- Transient aerosol concentration inversion method based on a novel weighted spatiotemporal kriging model Z. Tan et al. https://doi.org/10.1016/j.net.2026.104674
- Spatial dynamic variability of carbon monoxide and covariates over Punjab using the wavelet coherence approach A. Mariam et al. https://doi.org/10.1016/j.asr.2026.08.064
- Surface ozone trends and related mortality across the climate regions of the contiguous United States during the most recent climate period, 1991–2020 S. Mousavinezhad et al. https://doi.org/10.1016/j.atmosenv.2023.119693
- Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. https://doi.org/10.1029/2021GL093096
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Latest update: 15 Nov 2026
Short summary
This paper introduces a novel Lagrangian model (Concentration Trajectory of Air pollution with an Integrated Lagrangian model, C-TRAIL) for showing the source and receptor areas by following polluted air masses. To investigate the concentrations and trajectories of air masses simultaneously, we use the trajectory-grid (TG) Lagrangian advection model. The TG model follows the concentrations of representative air
packetsof species along trajectories determined by the wind field.
This paper introduces a novel Lagrangian model (Concentration Trajectory of Air pollution with...
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