Comparison of Source Apportionment and Sensitivity Analysis in a Particulate Matter Air Quality Model

2009 ◽  
Vol 43 (17) ◽  
pp. 6669-6675 ◽  
Author(s):  
Bonyoung Koo ◽  
Gary M. Wilson ◽  
Ralph E. Morris ◽  
Alan M. Dunker ◽  
Greg Yarwood
Keyword(s):  
Sensitivity Analysis ◽  
Particulate Matter ◽  
Air Quality ◽  
Source Apportionment ◽  
Air Quality Model ◽  
Quality Model

scholarly journals Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations

2006 ◽  
Vol 40 ◽  
pp. 563-573 ◽  
Author(s):  
Sun-Kyoung Park ◽  
Charles Evan Cobb ◽  
Katherine Wade ◽  
James Mulholland ◽  
Yongtao Hu ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Model Evaluation ◽  
Spatial Variations ◽  
Air Quality Model ◽  
Quality Model ◽  
Pollutant Concentrations
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