Biases in Air Quality Models Capturing Ozone Trends at the Urban, Regional and National Scales: Impacts on Relative Response Factors (RRFs)

2021 ◽  
pp. 118722
Author(s):  
Yongtao Hu ◽  
M. Talat Odman ◽  
Armistead G. Russell ◽  
Naresh Kumar ◽  
Eladio Knipping
Keyword(s):  
Air Quality ◽  
Response Factors ◽  
Quality Models ◽  
Relative Response ◽  
Ozone Trends

Поправочные коэффициенты в формулах для расчета содержания примесей: сущность, способы определения и их ограничения

2019 ◽  
Vol 53 (5) ◽  
pp. 55-60 ◽  
Author(s):  
Наум Аронович Эпштейн
Keyword(s):  
Correction Factors ◽  
Response Factors ◽  
Relative Response

Представлены формулы для расчета коэффициентов относительной чувствительности RRF (relative response factors) и поправочных коэффициентов F (correction factors) примесей, а также формулы, необходимые для понимания сущности коэффициентов RRF и F. Рассмотрены основные способы определения поправочных коэффициентов и их ограничения (условия, выполнение которых необходимо для корректного определения RRF и F). Эти ограничения не отражены в Европейской фармакопее и в Фармакопее США, но от их учета зависит правильность определения значений поправочных коэффициентов. Приведены примеры и даны рекомендации для надежного определения и правильного использования поправочных коэффициентов.

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.

Evaluating Regional-Scale Air Quality Models

2008 ◽  
pp. 412-419
Author(s):  
Alice B. Gilliland ◽  
James M. Godowitch ◽  
Christian Hogrefe ◽  
S. T. Rao
Keyword(s):  
Air Quality ◽  
Regional Scale ◽  
Quality Models
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