The Study of Ozone Climatology and Pollution in the Northeastern and Southern United States Using Regional Air Quality Models

1994 ◽  
pp. 96-119
Author(s):  
William L. Chameides
Keyword(s):  
United States ◽  
Air Quality ◽  
Southern United States ◽  
Quality Models ◽  
Regional Air Quality

scholarly journals Turbulent transport, emissions and the role of compensating errors in chemical transport models

2014 ◽  
Vol 7 (3) ◽  
pp. 1001-1024 ◽  
Author(s):  
P. A. Makar ◽  
R. Nissen ◽  
A. Teakles ◽  
J. Zhang ◽  
Q. Zheng ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Air Quality ◽  
Lower Limit ◽  
Turbulent Mixing ◽  
Turbulent Transport ◽  
Model Error ◽  
Quality Models ◽  
Regional Air Quality ◽  
Pm2.5 Emissions

Abstract. The balance between turbulent transport and emissions is a key issue in understanding the formation of O3 and particulate matter with diameters less than 2.5 μm (PM2.5). Discrepancies between observed and simulated concentrations for these species have, in the past, been ascribed to insufficient turbulent mixing, particularly for atmospherically stable environments. This assumption may be simplistic – turbulent mixing deficiencies may explain only part of these discrepancies, and as turbulence parameterizations are improved, the timing of primary PM2.5 emissions may play a much more significant role in the further reduction of model error. In a study of these issues, two regional air-quality models, the Community Multi-scale Air Quality model (CMAQ, version 4.6) and A Unified Regional Air-quality Modelling System (AURAMS, version 1.4.2), were compared to observations for a domain in north-western North America. The air-quality models made use of the same emissions inventory, emissions processing system, meteorological driving model, and model domain, map projection and horizontal grid, eliminating these factors as potential sources of discrepancies between model predictions. The initial statistical comparison between the models and monitoring network data showed that AURAMS' O3 simulations outperformed those of this version of CMAQ4.6, while CMAQ4.6 outperformed AURAMS for most PM2.5 statistical measures. A process analysis of the models revealed that many of the differences between the models' results could be attributed to the strength of turbulent diffusion, via the choice of an a priori lower limit in the magnitude of vertical diffusion coefficients, with AURAMS using 0.1 m2 s−1 and CMAQ4.6 using 1.0 m2 s−1. The use of the larger CMAQ4.6 value for the lower limit of vertical diffusivity within AURAMS resulted in a similar performance for the two models (with AURAMS also showing improved PM2.5, yet degraded O3, and a similar time series as CMAQ4.6). The differences between model results were most noticeable at night, when the higher minimum turbulent diffusivity resulted in an erroneous secondary peak in predicted night-time O3. A spatially invariant and relatively high lower limit in diffusivity could not reduce errors in both O3 and PM2.5 fields, implying that other factors aside from the strength of turbulence might be responsible for the PM2.5 over-predictions. Further investigation showed that the magnitude, timing and spatial allocation of area source emissions could result in improvements to PM2.5 performance with minimal O3 performance degradation. AURAMS was then used to investigate a land-use-dependant lower limit in diffusivity of 1.0 m2 s−1 in urban regions, linearly scaling to 0.01 m2s−1 in rural areas, as employed in CMAQ5.0.1. This strategy was found to significantly improve mean statistics for PM2.5 throughout the day and mean O3 statistics at night, while significantly degrading (halving) midday PM2.5 correlation coefficients and slope of observed to model simulations. Time series of domain-wide model error statistics aggregated by local hour were shown to be a useful tool for performance analysis, with significant variations in performance occurring at different hours of the day. The use of the land-use-dependant lower limit in diffusivity was also shown to reduce the model's sensitivity to the temporal allocation of its emissions inputs. The modelling scenarios suggest that while turbulence plays a key role in O3 and PM2.5 formation in urban regions, and in their downwind transport, the spatial and temporal allocation of primary PM2.5 emissions also has a potentially significant impact on PM2.5 concentration levels. The results show the complex nature of the interactions between turbulence and emissions, and the potential of the strength of the former to mask the impact of changes in the latter.

Cloud and Fog Effects and Their Parameterisation in Regional Air Quality Models

1997 ◽  
pp. 121-155 ◽  
Author(s):  
Günther Mauersberger ◽  
A. I. Flossmann ◽  
H. R. Pruppacher ◽  
William R. Stockwell ◽  
Thomas Schönemeyer ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Models ◽  
Regional Air Quality

scholarly journals Model sensitivity evaluation for organic carbon using two multi-pollutant air quality models that simulate regional haze in the southeastern United States

2006 ◽  
Vol 40 (26) ◽  
pp. 4960-4972 ◽  
Author(s):  
Ralph E. Morris ◽  
Bonyoung Koo ◽  
Alex Guenther ◽  
Greg Yarwood ◽  
Dennis McNally ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Organic Carbon ◽  
Southeastern United States ◽  
Model Sensitivity ◽  
Quality Models ◽  
Sensitivity Evaluation ◽  
Regional Haze

Evaluation of three regional air quality models

1985 ◽  
Vol 19 (7) ◽  
pp. 1103-1115 ◽  
Author(s):  
R.E. Ruff ◽  
K.C. Nitz ◽  
F.L. Ludwig ◽  
C.M. Bhumralkar ◽  
J.D. Shannon ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Models ◽  
Regional Air Quality

Regional Air Quality:  Local and Interstate Impacts of NOxand SO2Emissions on Ozone and Fine Particulate Matter in the Eastern United States

2007 ◽  
Vol 41 (13) ◽  
pp. 4677-4689 ◽  
Author(s):  
Michelle S. Bergin ◽  
Jhih-Shyang Shih ◽  
Alan J. Krupnick ◽  
James W. Boylan ◽  
James G. Wilkinson ◽  
...  
Keyword(s):  
United States ◽  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Eastern United States ◽  
Fine Particulate ◽  
Regional Air Quality

scholarly journals Technical Note – AQMEII4 Activity 1: Evaluation of Wet and Dry Deposition Schemes as an Integral Part of Regional-Scale Air Quality Models

2021 ◽  
Author(s):  
Stefano Galmarini ◽  
Paul Makar ◽  
Olivia Clifton ◽  
Christian Hogrefe ◽  
Jesse Bash ◽  
...  
Keyword(s):  
Air Quality ◽  
Dry Deposition ◽  
Model Evaluation ◽  
Regional Scale ◽  
Technical Note ◽  
Air Pollutant ◽  
Evaluation Activity ◽  
Quality Models ◽  
Wet And Dry Deposition ◽  
Regional Air Quality

Abstract. We present in this technical note the research protocol for Phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided in two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reducing input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (Part 2). The scope of these papers is to present the scientific protocols for AQMEII4, as well to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving land use/land cover (LULC)-specific diagnostics and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air-quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

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