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

Abstract

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.

Spatial-temporal variations in surface ozone in Northern China as observed during 2009–2010 and possible implications for future air quality control strategies

“…However, the increase in population and the number of motor vehicles has induced pollutants from other more complex sources and exhibited different spatialtemporal variations (Zhang et al, 1998;Hao et al, 2005;Chai et al, 2006;Tang et al, 2009). For instance, high levels of various volatile inorganic/organic compounds, photochemical oxidants and particulate matters have been reported for the area (Tang et al, 1995;Xu et al, 2005;Cheng et al, 2007;Chan and Yao, 2008;Xin et al, 2010). Worst of all, in stagnant weather for summer over the BTH region, atmospheric pollutants from various emissions easily accumulate and lead to serious pollution events, exhibiting decreased visibility, high concentration of particulate matter and ozone in the region (Ying et al, 1999;Song et al, 2002;Ding et al, 2005).…”

Variability and reduction of atmospheric pollutants in Beijing and its surrounding area during the Beijing 2008 Olympic Games

“…O3 and O x decreased from June to September 2008. As the autumn season began, the reduced rates of atmospheric photochem-…”

“…The correlation between the daily O3 and O x concentrations in Beijing and its surrounding area during the monitoring period.…”

Assessment of PM10 Emission Sources for Priority Regulation in Urban Air Quality Management Using a New Coupled MM5-CAMx-PSAT Modeling Approach

“…The overview of the Beijing metropolitan region can be found in Cheng et al (2007aCheng et al ( , 2007b and Huang et al (2010). For modeling purpose, the emission inventories of Beijing and its surrounding regions were obtained.…”

“…Particularly, in recent years, the development of air quality models with multi-scale and multi-pollutant forecasting capabilities has received significant attention to deal with regional air pollution issues under complex terrain and meteorological conditions. Three typical examples are the Model-3 Community Multi-scale Air Quality (Model-3/CMAQ) modeling system (Byun, 1999;Cheng et al, 2007aCheng et al, , 2007b, the Comprehensive Air Quality Model with Extensions (CAMx) (Titov et al, 2007), and the Weather Research and Forecasting/Chemistry (WRF/Chem) modeling system (Grell et al, 2005). For example, Cheng et al (2007aCheng et al ( , 2007b used an ARPS-CMAQ modeling approach to study the contributions of various emission sources to the ambient PM 10 concentrations in Beijing and to investigate the atmospheric assimilative capacity for regulating regional air pollutant emissions.…”