Abstract
Abstract. A source-oriented version of the Weather Research and Forecasting model with chemistry (SOWC, hereinafter) was developed. SOWC separately tracks primary particles with different hygroscopic properties rather than instantaneously combining them into an internal mixture. This approach avoids artificially mixing light absorbing black + brown carbon particles with materials such as sulfate that would encourage the formation of additional coatings. Source-oriented particles undergo coagulation and gas-particle conversion, but these processes are considered in a dynamic framework that realistically "ages" primary particles over hours and days in the atmosphere. SOWC more realistically predicts radiative feedbacks from anthropogenic aerosols compared to models that make internal mixing or other artificial mixing assumptions. A three-week stagnation episode (15 December 2000 to 6 January 2001) in the San Joaquin Valley (SJV) during the California Regional PM10 / PM2.5 Air Quality Study (CRPAQS) was chosen for the initial application of the new modeling system. Primary particles emitted from diesel engines, wood smoke, high-sulfur fuel combustion, food cooking, and other anthropogenic sources were tracked separately throughout the simulation as they aged in the atmosphere. Differences were identified between predictions from the source oriented vs. the internally mixed representation of particles with meteorological feedbacks in WRF/Chem for a number of meteorological parameters: aerosol extinction coefficients, downward shortwave flux, planetary boundary layer depth, and primary and secondary particulate matter concentrations. Comparisons with observations show that SOWC predicts particle scattering coefficients more accurately than the internally mixed model. Downward shortwave radiation predicted by SOWC is enhanced by ~1% at ground level chiefly because diesel engine particles in the source-oriented mixture are not artificially coated with material that increases their absorption efficiency. The extinction coefficient predicted by SOWC is reduced by an average of 0.012 km−1 (4.8%) in the SJV with a maximum reduction of ~0.2 km−1. Planetary boundary layer (PBL) height is increased by an average of 5.2 m (1.5%) with a~maximum of ~100 m in the SJV. Particulate matter concentrations predicted by SOWC are 2.23 μg m−3 (3.8%) lower than the average by the internally mixed version of the same model in the SJV because increased solar radiation at the ground increases atmospheric mixing. The changes in predicted meteorological parameters and particle concentrations identified in the current study stem from the mixing state of black carbon. The source-oriented model representation with realistic aging processes predicts that hydrophobic diesel engine particles remain largely uncoated over the +7 day simulation period, while the internal mixture model representation predicts significant accumulation of secondary nitrate and water on diesel engine particles. Similar results will likely be found in any air pollution stagnation episode that is characterized by significant particulate nitrate production. Future work should consider episodes where coatings are predominantly sulfate and/or secondary organic aerosol.
Highlights
IntroductionThe Weather Research and Forecast (WRF) model developed primarily by the National Center for Atmospheric Research (NCAR) and the National Oceanic and Atmospheric Administration (NOAA) is frequently used to predict meteorological conditions during stagnation events that lead to high concentrations of air pollutants (Borge et al, 2008; Hu et al, 2010; Huang et al, 2010; Zhang et al, 2012)
Primary particles emitted from diesel engines, wood smoke, high-sulfur fuel combustion, food cooking, and other anthropogenic sources were tracked separately throughout the simulation as they aged in the atmosphere
The Weather Research and Forecast (WRF) model developed primarily by the National Center for Atmospheric Research (NCAR) and the National Oceanic and Atmospheric Administration (NOAA) is frequently used to predict meteorological conditions during stagnation events that lead to high concentrations of air pollutants (Borge et al, 2008; Hu et al, 2010; Huang et al, 2010; Zhang et al, 2012)
Summary
The Weather Research and Forecast (WRF) model developed primarily by the National Center for Atmospheric Research (NCAR) and the National Oceanic and Atmospheric Administration (NOAA) is frequently used to predict meteorological conditions during stagnation events that lead to high concentrations of air pollutants (Borge et al, 2008; Hu et al, 2010; Huang et al, 2010; Zhang et al, 2012). The WRF model with chemistry (WRF/Chem) allows for coupled simulations of atmospheric chemistry and meteorology so that feedbacks can be considered (Chapman et al, 2009; Fast et al, 2006; Grell et al, 2005; Peckham et al, 2011) These feedback effects can be especially important during air pollution episodes that occur as a result of stagnation events, which are characterized by weak synoptic forcing of winds, leading to the buildup of pollutant concentrations close to emissions sources. Their study showed that an externally mixed representation will separate out species such as Na+ and SO24− that exist independently in the real world environment, while an internally mixed plume will combine these into one aerosol It was observed through their results that with increasing relative humidity, the external mixture model could accurately predict a mono-disperse.
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