Development and Implementation of the EPA’s Models-3 Initial Operating Version: Community Multi-Scale Air Quality (CMAQ) Model

Byun, Daewon W.; Ching, Jason; Novak, J.H.; Young, J. M.

doi:10.1007/978-1-4757-9128-0_37

Cited by 22 publications

(15 citation statements)

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“…Cloud fraction is estimated using relative humidity (RH) and critical RH (Byun and Ching, 1999). Cloud fraction (f k c ) above the boundary layer is…”

Section: Introductionmentioning

confidence: 99%

Evaluation of modeled surface ozone biases as a function of cloud cover fraction

et al. 2015

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Abstract. A regional air-quality forecast system's model of surface ozone variability based on cloud coverage is evaluated using satellite-observed cloud fraction (CF) information and a surface air-quality monitoring system. We compared CF and daily maximum ozone from the National Oceanic and Atmospheric Administration's National Air Quality Forecast Capability (NOAA NAQFC) with CFs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Environmental Protection Agency's AirNow surface ozone measurements during May to October 2014. We found that observed surface ozone shows a negative correlation with the MODIS CFs, showing around 1 ppb decrease for 10 % MODIS CF change over the contiguous United States, while the correlation of modeled surface ozone with the model CFs is much weaker, showing only −0.5 ppb per 10 % NAQFC CF change. Further, daytime CF differences between MODIS and NAQFC are correlated with modeled surface-ozone biases between AirNow and NAQFC, showing −1.05 ppb per 10 % CF change, implying that spatial and temporal misplacement of the modeled cloud field might have biased modeled surface ozone level. Current NAQFC cloud fields seem to have fewer CFs compared to MODIS cloud fields (mean NAQFC CF = 0.38 and mean MODIS CF = 0.55), contributing up to 35 % of surface-ozone bias in the current NAQFC system.

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“…Cloud fraction is estimated using relative humidity (RH) and critical RH (Byun and Ching, 1999). Cloud fraction (f k c ) above the boundary layer is…”

Section: Introductionmentioning

confidence: 99%

Evaluation of modeled surface ozone biases as a function of cloud cover fraction

et al. 2015

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“…To assess air quality in the YRD, the US EPA's "One Atmosphere" model, the Community Multi-scale Air Quality Modeling System (CMAQ) (Dennis et al, 1996;Byun et al, 1998;Byun and Ching, 1999), is used to simulate the chemical transport of air pollutants, driven by a new emission inventory. CMAQ is a chemical dispersion model representative of the third generation of air quality models and includes ozone, aerosols, acid deposition, visibility, and air toxics chemistry.…”

Section: Introductionmentioning

confidence: 99%

Air quality and emissions in the Yangtze River Delta, China

et al. 2011

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Abstract. Regional trans-boundary air pollution has become an important issue in the field of air pollution modeling. This paper presents the results of the implementation of the MM5-CMAQ modeling system in the Yangtze River Delta (YRD) for the months of January and July of 2004. The meteorological parameters are obtained by using the MM5 model. A new regional emission inventory with spatial and temporal allocations based on local statistical data has been developed to provide input emissions data to the MM5-CMAQ modeling system. The pollutant concentrations obtained from the MM5-CMAQ modeling system have been compared with observational data from the national air pollution monitoring network. It is found that air quality in winter in the YRD is generally worse than in summer, due mainly to unfavorable meteorological dispersion conditions. In winter, the pollution transport from Northern China to the YRD reinforces the pollution caused by large local emissions. The monthly average concentration of SO 2 in the YRD is 0.026 ± 0.011 mg m −3 in January and 0.017 ± 0.009 mg m −3 in July. Monthly average concentrations of NO 2 in the YRD in January and July are 0.021 ± 0.009 mg m −3 , and 0.014 ± 0.008 mg m −3 , respectively. The monthly average concentration of PM 10 in the YRD is 0.080 ± 0.028 mg m −3 in January and 0.025 ± 0.015 mg m −3 in July. Visibility is also a problem, with average deciview values of 26.4 ± 2.95 dcv in winter and 17.6 ± 3.3 dcv in summer. The ozone concentration in the downtown area of a city like Correspondence to: C. H. Chen (chench@saes.sh.cn) Zhoushan can be very high, with the highest simulated value reaching 0.24 mg m −3 . In January, the monthly average concentration of O 3 in the YRD is 0.052 ± 0.011 mg m −3 , and 0.054 ± 0.008 mg m −3 in July. Our results show that ozone and haze have become extremely important issues in the regional air quality. Thus, regional air pollution control is urgently needed to improve air quality in the YRD.

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“…On the other side, the Models-3/CMAQ air quality modeling system is a sophisticated modeling system which can handle research and application issues for multi-scale (urban and regional) and multipollutant (oxidants, acid deposition, and particulates) air quality prediction problems. It includes multiple capabilities of simulating concentrations of fine particulate, tropospheric ozone, acid deposition, visibility and other air pollutants in the atmosphere involving complex pollutant interactions (Byun, 1999a(Byun, , 1999bByun et al, 1997). In this study, the modeling domain with a spatial resolution of 4×4 km and a dimension of 49×49 grid cells covers the entire Beijing metropolitan region (Fig.…”

Section: Arps/cmaq Air Quality Modeling Systemmentioning

confidence: 99%

An ARPS-CMAQ Modeling Approach for Assessing the Atmospheric Assimilative Capacity of the Beijing Metropolitan Region

Cheng

Chen

et al. 2007

Water Air Soil Pollut

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A coupled ARPS-CMAQ modeling system was applied to investigate the atmospheric assimilative capacity (AAC) of PM 10 in the Beijing metropolitan region of China. The AAC was defined as the maximum allowable pollutant emission that can be discharged to the atmosphere without violating the desired air quality objective in the planning region. The coupled modeling system was firstly evaluated through comparing the simulation results of PM 10 concentration with the corresponding observations during four representative months in 2002, which showed an acceptable modeling performance. By using a trial-and-error approach, the validated modeling system was run through gradually reducing the emission strength within the modeling domain from a GIS-based pollutant emission database until a desired air quality objective was achieved. The air quality objective was characterized by the air quality guideline-satisfaction ratio (AQGSR) which was defined as the ratio of days when the daily PM 10 concentrations are below the air quality standard during a certain time period. The PM 10 emission within the modeling domain after such trial-and-error reduction under the condition of satisfying a certain AQGSR was considered its corresponding assimilative capacity. Accordingly, the atmospheric assimilative capacities of PM 10 in the study region were identified corresponding to the AQGSR of 60, 65, 70, 75 and 80%, respectively. The relation between such capacity and guidelinesatisfaction ratio was then established through regression analysis. The results could provide sound basis for decision makers in terms of effective air quality management by understanding the allowable pollutant discharge under different desired air quality objectives.

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Development and Implementation of the EPA’s Models-3 Initial Operating Version: Community Multi-Scale Air Quality (CMAQ) Model

Cited by 22 publications

References 4 publications

Evaluation of modeled surface ozone biases as a function of cloud cover fraction

Evaluation of modeled surface ozone biases as a function of cloud cover fraction

Air quality and emissions in the Yangtze River Delta, China

An ARPS-CMAQ Modeling Approach for Assessing the Atmospheric Assimilative Capacity of the Beijing Metropolitan Region

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