Air quality simulations for North America - MM5–CAMx modelling performance for main gaseous pollutants

Ferreira, Joana; Rodríguez, Antonio Pérez; Monteiro, A.; Miranda, Ana Isabel; Dios, M.; Souto, José A.; Yarwood, Greg; Nopmongcol, Uarporn; Borrego, Carlos

doi:10.1016/j.atmosenv.2011.10.020

Cited by 15 publications

(7 citation statements)

References 42 publications

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“…They suggested that vertical mixing was one cause of such discrepancies. Ferreira et al () showed that numerical models tend to underestimate O 3 and overestimate NO 2 and CO at night as a consequence of underestimation of the Planetary Boundary Layer height in North America. Zhang et al () showed that CMAQ/WRF‐Chem (Weather Research and Forecasting) significantly overpredicts O 3 in East Asia and suggested that many factors including uncertainties in emissions and upper boundary conditions can contribute to the errors.…”

Section: Resultsmentioning

confidence: 99%

Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

et al. 2018

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Based on prevailing numerical forecasting models (Community Multiscale Air Quality [CMAQ] model , Comprehensive Air Quality Model with Extensions, and Nested Air Quality Prediction Modeling System) and observations from monitoring stations in Hong Kong, we employ a set of autoregressive integrated moving average (ARIMA) models with numerical forecasts (ARIMAX) to improve the forecast of air pollutants including PM2.5, NO2, and O3. The results show significant improvements in multiple evaluation metrics for daily (1–3 days) and hourly (1–72 hr) forecast. Forecasts on daily 1‐hr and 8‐hr maximum O3 are also improved. For instance, compared with CMAQ, applying CMAQ‐ARIMA reduces average root‐mean‐square errors (RMSEs) at all stations for daily average PM2.5, NO2, and O3 in the next 3 days by 14.3–21.0%, 41.2–46.3%, and 47.8–49.7%, respectively. For hourly forecasts in the next 72 hr, reductions in RMSEs brought by ARIMAX using CMAQ are 18.2% for PM2.5, 32.1% for NO2, and 36.7% for O3. Large improvements in RMSEs are achieved for nonrural PM2.5 and rural NO2 using ARIMAX with three numerical models. Dynamic hourly forecast shows that ARIMAX can be applied for forecast of 7‐ to 72‐hr PM2.5, 4‐ to 72‐hr NO2, and 4‐ to 6‐hr O3. Besides applying ARIMAX for NO2, we recommend a mixed forecast strategy to ARIMAX for normal values of PM2.5 and O3 and employ numerical models for outputs above 75th percentile of historical observations. Our hybrid ARIMAX method can combine the advantage of ARIMA and numerical modeling to assist real‐time air quality forecasting efficiently and consistently.

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Section: Resultsmentioning

confidence: 99%

Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

et al. 2018

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“…CAMx is a 3D chemistrytransport Eulerian photochemical model that allows for an integrated assessment of gaseous and particulate air pollution over many scales, ranging from sub-urban to continental. CAMx is well-known and has been extensively applied for Portugal and worldwide Ferreira et al, 2012Ferreira et al, , 2010Huang et al, 2010;Borrego et al, 2008). Figure 1.…”

Section: Modelling Approachmentioning

confidence: 99%

Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling

Fernandes

Riffler

Ferreira

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Satellite data provide high spatial coverage and characterization of atmospheric components for vertical column. Additionally, the use of air pollution modelling in combination with satellite data opens the challenging perspective to analyse the contribution of different pollution sources and transport processes. The main objective of this work is to study the AOD over Portugal using satellite observations in combination with air pollution modelling. For this purpose, satellite data provided by Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on-board the geostationary Meteosat-9 satellite on AOD at 550 nm and modelling results from the Chemical Transport Model (CAMx -Comprehensive Air quality Model) were analysed. The study period was May 2011 and the aim was to analyse the spatial variations of AOD over Portugal. In this study, a multi-temporal technique to retrieve AOD over land from SEVIRI was used. The proposed method takes advantage of SEVIRI's high temporal resolution of 15 minutes and high spatial resolution. CAMx provides the size distribution of each aerosol constituent among a number of fixed size sections. For post processing, CAMx output species per size bin have been grouped into total particulate sulphate (PSO4), total primary and secondary organic aerosols (POA + SOA), total primary elemental carbon (PEC) and primary inert material per size bin (CRST_1 to CRST_4) to be used in AOD quantification. The AOD was calculated by integration of aerosol extinction coefficient (Qext) on the vertical column. The results were analysed in terms of temporal and spatial variations. The analysis points out that the implemented methodology provides a good spatial agreement between modelling results and satellite observation for dust outbreak studied (10th -17th of May 2011). A correlation coefficient of r=0.79 was found between the two datasets. This work provides relevant background to start the integration of these two different types of the data in order to improve air pollution assessment.

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“…Many air quality models can skillfully simulate surface ozone in North America for focused studies of certain time periods [Hogrefe et al, 2004;Appel et al, 2007;Ferreira et al, 2011;Appel et al, 2012;Simon et al, 2012]. Global models can reflect changes in ozone resulting from control measures [e.g., Clifton et al, 2014;Rieder et al, 2015], especially for rural sites representative of regional atmospheric composition, but nonattainment is based on monitors with the highest readings.…”

Section: Introductionmentioning

confidence: 99%

CAMx ozone source attribution in the eastern United States using guidance from observations during DISCOVER‐AQ Maryland

Goldberg

Vinciguerra

Anderson

et al. 2016

Geophysical Research Letters

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A Comprehensive Air‐Quality Model with Extensions (CAMx) version 6.10 simulation was assessed through comparison with data acquired during NASA's 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER‐AQ) Maryland field campaign. Comparisons for the baseline simulation (Carbon Bond 2005 (CB05) chemistry, Environmental Protection Agency 2011 National Emissions Inventory) show a model overestimate of NOy by +86.2% and an underestimate of HCHO by −28.3%. We present a new model framework (Carbon Bond 6 Revision 2 chemistry (CB6r2), Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 biogenic emissions, 50% reduction in mobile NOx, enhanced representation of isoprene nitrates) that better matches observations. The new model framework attributes 31.4% more surface ozone in Maryland to electric generating units (EGUs) and 34.6% less ozone to on‐road mobile sources. Surface ozone becomes more NOx limited throughout the eastern United States compared to the baseline simulation. The baseline model therefore likely underestimates the effectiveness of anthropogenic NOx reductions as well as the current contribution of EGUs to surface ozone.

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Air quality simulations for North America - MM5–CAMx modelling performance for main gaseous pollutants

Cited by 15 publications

References 42 publications

Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling

CAMx ozone source attribution in the eastern United States using guidance from observations during DISCOVER‐AQ Maryland

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