Articles | Volume 14, issue 2
https://doi.org/10.5194/acp-14-675-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-14-675-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
22 Jan 2014
Research article |
| 22 Jan 2014
The impact of satellite-adjusted NOx emissions on simulated NOx and O3 discrepancies in the urban and outflow areas of the Pacific and Lower Middle US
Department of Earth and Atmospheric Sciences,University of Houston, 312 Science & Research Building 1, Houston, TX 77204, USA
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Cited
16 citations as recorded by crossref.
- Constraining NOx emissions using satellite NO2 measurements during 2013 DISCOVER-AQ Texas campaign A. Souri et al. https://doi.org/10.1016/j.atmosenv.2016.02.020
- Impact of updated traffic emissions on HONO mixing ratios simulated for urban site in Houston, Texas B. Czader et al. https://doi.org/10.5194/acp-15-1253-2015
- Effect of Ambient Temperature on Total Organic Gas Speciation Profiles from Light-Duty Gasoline Vehicle Exhaust A. Roy et al. https://doi.org/10.1021/acs.est.6b01081
- The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign X. Li et al. https://doi.org/10.5194/acp-16-3127-2016
- A 15-year climatology of wind pattern impacts on surface ozone in Houston, Texas A. Souri et al. https://doi.org/10.1016/j.atmosres.2016.02.007
- Impact of high-resolution sea surface temperature, emission spikes and wind on simulated surface ozone in Houston, Texas during a high ozone episode S. Pan et al. https://doi.org/10.1016/j.atmosenv.2016.12.030
- A real-time hourly ozone prediction system using deep convolutional neural network E. Eslami et al. https://doi.org/10.1007/s00521-019-04282-x
- Modeling the uncertainty of several VOC and its impact on simulated VOC and ozone in Houston, Texas S. Pan et al. https://doi.org/10.1016/j.atmosenv.2015.09.029
- A quantitative analysis of grid nudging effect on each process of PM 2.5 production in the Korean Peninsula W. Jeon et al. https://doi.org/10.1016/j.atmosenv.2015.10.050
- Regional background O3 and NOx in the Houston–Galveston–Brazoria (TX) region: a decadal-scale perspective L. Suciu et al. https://doi.org/10.5194/acp-17-6565-2017
- Allocating emissions to 4 km and 1 km horizontal spatial resolutions and its impact on simulated NOx and O3 in Houston, TX S. Pan et al. https://doi.org/10.1016/j.atmosenv.2017.06.026
- Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system E. Eslami et al. https://doi.org/10.5194/gmd-13-6237-2020
- Chemical condition and surface ozone in large cities of Texas during the last decade: Observational evidence from OMI, CAMS, and model analysis Y. Choi & A. Souri https://doi.org/10.1016/j.rse.2015.06.026
- A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks E. Eslami et al. https://doi.org/10.1007/s00521-019-04287-6
- Impacts of TROPOMI-Derived NOX Emissions on NO2 and O3 Simulations in the NCP during COVID-19 Y. Zhu et al. https://doi.org/10.1021/acsenvironau.2c00013
- Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species N. Senthilkumar et al. https://doi.org/10.1016/j.atmosenv.2022.118982
16 citations as recorded by crossref.
- Constraining NOx emissions using satellite NO2 measurements during 2013 DISCOVER-AQ Texas campaign A. Souri et al. https://doi.org/10.1016/j.atmosenv.2016.02.020
- Impact of updated traffic emissions on HONO mixing ratios simulated for urban site in Houston, Texas B. Czader et al. https://doi.org/10.5194/acp-15-1253-2015
- Effect of Ambient Temperature on Total Organic Gas Speciation Profiles from Light-Duty Gasoline Vehicle Exhaust A. Roy et al. https://doi.org/10.1021/acs.est.6b01081
- The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign X. Li et al. https://doi.org/10.5194/acp-16-3127-2016
- A 15-year climatology of wind pattern impacts on surface ozone in Houston, Texas A. Souri et al. https://doi.org/10.1016/j.atmosres.2016.02.007
- Impact of high-resolution sea surface temperature, emission spikes and wind on simulated surface ozone in Houston, Texas during a high ozone episode S. Pan et al. https://doi.org/10.1016/j.atmosenv.2016.12.030
- A real-time hourly ozone prediction system using deep convolutional neural network E. Eslami et al. https://doi.org/10.1007/s00521-019-04282-x
- Modeling the uncertainty of several VOC and its impact on simulated VOC and ozone in Houston, Texas S. Pan et al. https://doi.org/10.1016/j.atmosenv.2015.09.029
- A quantitative analysis of grid nudging effect on each process of PM 2.5 production in the Korean Peninsula W. Jeon et al. https://doi.org/10.1016/j.atmosenv.2015.10.050
- Regional background O3 and NOx in the Houston–Galveston–Brazoria (TX) region: a decadal-scale perspective L. Suciu et al. https://doi.org/10.5194/acp-17-6565-2017
- Allocating emissions to 4 km and 1 km horizontal spatial resolutions and its impact on simulated NOx and O3 in Houston, TX S. Pan et al. https://doi.org/10.1016/j.atmosenv.2017.06.026
- Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system E. Eslami et al. https://doi.org/10.5194/gmd-13-6237-2020
- Chemical condition and surface ozone in large cities of Texas during the last decade: Observational evidence from OMI, CAMS, and model analysis Y. Choi & A. Souri https://doi.org/10.1016/j.rse.2015.06.026
- A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks E. Eslami et al. https://doi.org/10.1007/s00521-019-04287-6
- Impacts of TROPOMI-Derived NOX Emissions on NO2 and O3 Simulations in the NCP during COVID-19 Y. Zhu et al. https://doi.org/10.1021/acsenvironau.2c00013
- Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species N. Senthilkumar et al. https://doi.org/10.1016/j.atmosenv.2022.118982
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