A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter

Community Multi-Scale Air Quality （CMAQ） estimates of sulfates, nitrates, ammonium, and organic carbon are highly influenced by uncertainties in modeled secondary formation processes, such as chemical mechanisms, volatilization, and condensation rates. These compounds constitute the majority ofPM2.5 mass, and reducing bias in estimated concentrations has benefits for policy measures and epidemiological studies. In this work, a method for adjusting source impacts on secondary species is developed that provides estimates of source contributions and reduces bias in modeled concentrations compared to observations. The bias correction adjusts concentrations and source impacts based on the difference between modeled concentrations and observations while taking into account uncertainties at the location of interest; and it is applied both spatially and temporally. We apply the method over the US for 2006. The mean bias for initial CMAQ concentrations compared to observations is -0.28 （OC）, 0.11 （NO3）, 0.05 （NH4）, and -0.08 （SO4）. The normalized mean bias in modeled concentrations compared to observations was effectively zero for OC, NO3, NH4, and SO4 after applying the secondary bias correction. Ten-fold cross-validation was conducted to determine the performance of the spatial application of the bias correction. Cross-validation performance was favorable; correlation coefficients were greater than 0.69 for all species when comparing observations and concentrations based on kriged correction factors. The methods presented here address model uncertainties by improving simulated concentrations and source impacts of secondary particulate matter through data assimilation. Secondary-adjusted concentrations and source impacts from 20 emissions sources are generated for 2006 over continental US.

Variation of PM_(2.5) concentration in Hangzhou,China

This observational study investigates the variation of PM2.5 concentration and its ratio against PM10 concentration under different weather systems and pollution types. The study was conducted in Hangzhou on east China＇s Yangtze River Delta using data collected at seven ambient air quality monitoring stations around the metropolitan area between 2006 and 2008 and using weather information in the same period. Nine predominant weather systems affecting the city were classified through careful analysis of the 11- year surface and upper air weather charts from 1996 to 2006. Each observational day was then assigned to one of the nine weather systems. It was found that the PM2.5 concentration varied greatly for different weather systems, with the highest PM2.5 concentration associated with the post-cold-frontal system at 0.091 mg/m^3 and the lowest PM2.5 concentration with the easterlies system at 0.038 mg/m^3, although the PM2.5/PM10 ratio remained consistently above 0.5 for all systems. The post-cold-frontal system typically occurs in autumn and winter while the easterlies system is more a summer phenomenon. Among all types of pollution, the highest PM2.5 concentration of 0.117 mg/m^3 coincided with the large-scale continuous pollution events, suggesting that this type of pollution was more conducive to the formation of secondary particulate matters. The ratio of PM2.5/PM10 was above 0.5 in non-pollution days and all pollution types but one under the influence of dust storms when the ratio decreased to 0.3 or less. The outcomes of this study could be used to develop a rudimental predictive model of PM2.5 concentration based on weather system and pollution type.