Articles | Volume 20, issue 3
https://doi.org/10.5194/acp-20-1627-2020
© 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/acp-20-1627-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Feb 2020
Research article |
| 10 Feb 2020
| 10 Feb 2020
On the limit to the accuracy of regional-scale air quality models
Department of Marine, Earth, and Atmospheric Sciences, North Carolina
State University, Raleigh, NC, USA
Department of Civil and Environmental Engineering, University of
Connecticut, Storrs, CT, USA
Huiying Luo
Department of Civil and Environmental Engineering, University of
Connecticut, Storrs, CT, USA
Marina Astitha
Department of Civil and Environmental Engineering, University of
Connecticut, Storrs, CT, USA
Christian Hogrefe
Center for Environmental Measurement and Modeling, U.S. Environmental
Protection Agency, Research Triangle Park, NC, USA
Valerie Garcia
Center for Environmental Measurement and Modeling, U.S. Environmental
Protection Agency, Research Triangle Park, NC, USA
Rohit Mathur
Center for Environmental Measurement and Modeling, U.S. Environmental
Protection Agency, Research Triangle Park, NC, USA
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Cited
17 citations as recorded by crossref.
- A trajectory mining framework for exploring individual concurrent environmental exposure and its association with mental health Z. Kan et al.
- Seasonal Characteristics of Forecasting Uncertainties in Surface PM2.5 Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region Q. Du et al.
- Improving Deterministic Air Quality Forecasts Using Supervised Machine Learning: A Feasibility Study L. Łobocki
- Estimating black carbon emission using a simple Eulerian box model Y. Pan & Z. Song
- A review of international experience in air quality assessment M. Pozdnyakov et al.
- Predicting Seasonal Variations in River Water Quality: An Artificial Intelligence (AI) Approach Integrating Physicochemical Parameters H. Shawon et al.
- Assessing the manageable portion of ground-level ozone in the contiguous United States H. Luo et al.
- A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance A. Sayeed et al.
- Air quality modeling in the metropolitan area of São Paulo, Brazil: A review M. Gavidia-Calderón et al.
- Characterizing variability and predictability for air pollutants with stochastic models P. Meyer et al.
- Influence of the Grid Resolutions on the Computer-Simulated Surface Air Pollution Concentrations in Bulgaria G. Gadzhev et al.
- Advances in air quality research – current and emerging challenges R. Sokhi et al.
- Evaluating the ability of the Wind Erosion Prediction System (WEPS) to simulate near-surface wind speeds in the Inland Pacific Northwest, USA X. Zhang et al.
- Uncertainty analysis of modeled ozone changes due to anthropogenic emission reductions in Eastern Texas A. Dunker et al.
- Locating Low-Cost Air Quality Monitoring Devices in Low-Resource Regions Is Not Enough to Acquire Robust Air Quality Data Usable for Policy Decisions A. Emekwuru et al.
- Exploring the potential impacts of anthropogenic heating on urban climate during heatwaves A. Khan et al.
- A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment I. Stergiou et al.
17 citations as recorded by crossref.
- A trajectory mining framework for exploring individual concurrent environmental exposure and its association with mental health Z. Kan et al.
- Seasonal Characteristics of Forecasting Uncertainties in Surface PM2.5 Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region Q. Du et al.
- Improving Deterministic Air Quality Forecasts Using Supervised Machine Learning: A Feasibility Study L. Łobocki
- Estimating black carbon emission using a simple Eulerian box model Y. Pan & Z. Song
- A review of international experience in air quality assessment M. Pozdnyakov et al.
- Predicting Seasonal Variations in River Water Quality: An Artificial Intelligence (AI) Approach Integrating Physicochemical Parameters H. Shawon et al.
- Assessing the manageable portion of ground-level ozone in the contiguous United States H. Luo et al.
- A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance A. Sayeed et al.
- Air quality modeling in the metropolitan area of São Paulo, Brazil: A review M. Gavidia-Calderón et al.
- Characterizing variability and predictability for air pollutants with stochastic models P. Meyer et al.
- Influence of the Grid Resolutions on the Computer-Simulated Surface Air Pollution Concentrations in Bulgaria G. Gadzhev et al.
- Advances in air quality research – current and emerging challenges R. Sokhi et al.
- Evaluating the ability of the Wind Erosion Prediction System (WEPS) to simulate near-surface wind speeds in the Inland Pacific Northwest, USA X. Zhang et al.
- Uncertainty analysis of modeled ozone changes due to anthropogenic emission reductions in Eastern Texas A. Dunker et al.
- Locating Low-Cost Air Quality Monitoring Devices in Low-Resource Regions Is Not Enough to Acquire Robust Air Quality Data Usable for Policy Decisions A. Emekwuru et al.
- Exploring the potential impacts of anthropogenic heating on urban climate during heatwaves A. Khan et al.
- A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment I. Stergiou et al.
Saved (final revised paper)
Latest update: 29 Apr 2026
Short summary
Since numerical air quality models do not explicitly simulate stochastic variations in the atmosphere, there will always be differences between modeled and measured pollutant levels even when the model's physics, chemistry, numerical analysis, and its input data are perfect. This paper quantifies the inherent uncertainty in regional models due to the stochastic nature of the atmosphere. A knowledge of the expected error helps model developers in evaluating the real progress in improving models.
Since numerical air quality models do not explicitly simulate stochastic variations in the...
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